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1
Tavares, Nuno Filipe Ramalho da Cunha. "Multivariate analysis applied to clinical analysis data." Master's thesis, Faculdade de Ciências e Tecnologia, 2014. http://hdl.handle.net/10362/12288.
Texte intégralRésumé :
Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial<br>Folate, vitamin B12, iron and hemoglobin are essential for metabolic functions in the body. The deficiency of these can be the cause of several known pathologies and, untreated, can be responsible for severe morbidity and even death. The objective of this study is to characterize a population, residing in the metropolitan area of Lisbon and Setubal, concerning serum levels of folate, vitamin B12, iron and hemoglobin, as well as finding evidence of correlations between these parameters and illnesses, mainly cardiovascular, gastrointestinal, neurological and anemia.
Clinical analysis data was collected and submitted to multivariate analysis. First the data was screened with Spearman correlation and Kruskal-Wallis analysis of variance to study correlations and variability between groups. To characterize the population, we used cluster analysis with Ward’s linkage method. Finally a sensitivity analysis was performed to strengthen the results.
A positive correlation between iron with, ferritin and transferrin, and with hemoglobin was observed with the Spearman correlation. Kruskal-Wallis analysis of variance test showed significant differences between these biomarkers in persons aged 0 to 29, 30 to 59 and over 60 years old. Cluster analysis proved to be a useful tool when characterizing a population based on its biomarkers, showing evidence of low folate levels for the population in general, and hemoglobin levels below the reference values. Iron and vitamin B12 were within the reference range for most of the population. Low levels of the parameters were registered mainly in patients with cardiovascular, gastrointestinal, and neurological diseases and anemia.
2
Zappi, Alessandro <1990>. "Chemometrics applied to direct multivariate analysis." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/8898/1/Zappi_Alessandro_Thesis.pdf.
Texte intégralRésumé :
The present Ph.D. Thesis is focused on applications and developments of chemometrics. After a short introduction about chemometrics (Chapter 1), the present work is divided in three Chapters, reflecting the research activities addressed during the three-year PhD work:
• Chapter 2 concerns the application of classification tools to food traceability (Chapter 2.1), plant metabolomics (Chapter 2.2), and food-frauds detection (Chapter 2.3) problems.
• Chapter 3 concerns the application of design of experiments for a bio-remediation research (Chapter 3.1) and for machine optimization (Chapter 3.2).
• Chapter 4 concerns the development of the net analyte signal (NAS) procedure and its application to several analytical problems. The main aim of this research is to face the matrix-effect problem using a multivariate approach.
Chemometrics is the science that extracts useful information from chemical data. The development of instruments and computers is bringing to analytical methodologies ever more sophisticated, and the consequence is that huge amounts of data are collected. In parallel with this rapid evolution, it is, therefore, important to develop chemometric methods able to handle and process the data. Moreover, the attention is also focusing on analytical techniques that do not destroy the analyzed samples. Chemometrics and its application to non-destructive analytical methods are the main topics of this research project.
Several analytical techniques have been used during this project: gas-chromatography (GC), bioluminescence, atomic absorption spectroscopy (AAS), liquid chromatography (HPLC), near-infrared spectroscopy, UV-Vis spectroscopy, Raman spectroscopy, X-ray powder diffraction (XRPD), attenuated total reflectance (ATR) spectroscopy.
Moreover, this research activity was carried out in collaboration with several external research groups and companies
3
Campbell, Allen Webb. "Applied statisical analysis software system." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-08182009-040351/.
Texte intégral
4
Van, der Bijl Rinske. "Multivariate refinable functions with emphasis on box splines." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/2743.
Texte intégralRésumé :
Thesis (MComm (Mathematics))--Stellenbosch University, 2008.<br>The general purpose of this thesis is the analysis of multivariate refinement equations, with
focus on the bivariate case. Since box splines are the main prototype of such equations
(just like the cardinal B-splines in the univariate case), we make them our primary subject
of discussion throughout. The first two chapters are indeed about the origin and definition
of box splines, and try to elaborate on them in sufficient detail so as to build on them
in all subsequent chapters, while providing many examples and graphical illustrations to
make precise every aspect regarding box splines that will be mentioned.
Multivariate refinement equations are ones that take on the form
(x) =Xi2Zn
pi (Mx − i), (1)
where is a real-valued function, called a refinable function, on Rn, p = {pi}i2Zn is a
sequence of real numbers, called a refinement mask, and M is an n × n matrix with
integer entries, called a dilation matrix.
It is important to note that any such equation is thus simultaneously determined by all
three of , p and M — and the thesis will try and explain what role each of these plays
in a refinement equation.
In Chapter 3 we discuss the definition of refinement equations in more detail and elaborate
on box splines as our first examples of refinable functions, also showing that one can
actually use them to create even more such functions. Also observing from Chapter iii
iv
2 that box splines demand yet another parameter from us, namely an initial direction
matrix D, we focus on the more general instances of these in Chapter 4, while keeping
the dilation matrix M fixed. Chapter 5 then in turn deals with the matrix M and tries to
generalize some of the results found in Chapter 3 accordingly, keeping the initial direction
matrix fixed.
Having dealt with the refinement equation itself, we subsequently focus our attention on
the support of a (bivariate) refinable function — that is, the part of the xy-grid on which
such a function “lives” — and that of a refinement mask, in Chapter 6, and obtain a few
results that are in a sense introductory to our work in the next chapter.
Next, we move on to discuss one area in which refinable functions are especially applicable,
namely subdivision, which is analyzed in Chapter 7. After giving the basic definitions of
subdivision and subdivision convergence, and investigating the “sum rules” in Section 7.1,
we prove our main subdivision convergence result in Section 7.2. The chapter is concluded
with some examples in Section 7.3.
The thesis is concluded, in Chapter 8, with a number of remarks on what has been done
and issues that are left for future research.
5
TARIS, ALESSANDRA. "Multivariate techniques applied on spectroscopic data for process analysis and monitoring." Doctoral thesis, Università degli Studi di Cagliari, 2017. http://hdl.handle.net/11584/249570.
Texte intégralRésumé :
L’analisi e il monitoraggio di processo sono diventati di fondamentale importanza per garantire le prestazioni del processo e mantenere la qualità del prodotto. A tal scopo, la spettroscopia rappresenta uno strumento innovativo che permette di superare le problematiche che si incontrano con le tecniche analitiche convenzionali (per esempio, la gas cromatografia), poichè è veloce e non distruttiva e può fornire informazioni sullo stato chimico del processo in tempo reale. Tuttavia, a causa della grande quantità di informazioni presenti nelle misure raccolte, l’interpretazione e l’estrazione di informazione non è un compito semplice. A tal proposito, le tecniche multivariate agevolano significativamente il trattamento dei dati e permettono di inferire informazioni sul sistema analizzato.
In questa tesi, quattro sistemi sono indagati mediante misure spettroscopiche per mostrare la varietà di problemi che possono sorgere quando si trattano dati complessi e altamente informativi provenienti da differenti tecniche spettroscopiche. Per questo motivo, sono state esplorate differenti tecniche multivariate e sono mostrate le loro potenzialità e limitazioni: (i) si suggeriscono strategie basate sulla Principal Component Analysis e Partial Least Squares Regression per un migliore e più robusto monitoraggio di qualità dei detergenti commerciali liquidi; (ii) la Moving Window Principal Component Analysis è proposta per il monitoraggio di processi che si evolvono come la cristallizzazione di un Ingrediente Farmaceutico Attivo per identificare la nucleazione; (iii) la Time Window Statistical Total Correlation Spectroscopy insieme alla Multivariate Curve Resolution sono proposte per indagare la reazione di formazione di un materiale cementizio; (iv) la Multivariate Curve Resolution è utilizzata per ottenere informazioni sulla dissoluzione nello spazio e nel tempo di una pasta costituita da tensioattivi a partire da dati iperspettrali .
Perciò, le tecniche multivariate applicate a dati spettroscopici si dimostrano capaci di raggiungere i seguenti risultati:
a) Nel caso di detergenti commerciali, le osservazioni che non rispecchiano le condizioni di riferimento sono classificate correttamente. Inoltre, l’approccio proposto identifica quando la stima della concentrazione dei composti non può essere considerata accurata;
b) Riguardo la cristallizzazione dell’ingrediente farmaceutico, la nucleazione è stata individuata in modo accurato;
c) Gli spettri e la concentrazione dei composti coinvolti nella reazione di presa di un materiale cementizio sono stati stimati e l’evoluzione temporale del processo può essere seguita;
d) La velocità di dissoluzione dei tensioattivi presenti nella pasta è stata valutata.
Di conseguenza, i metodi multivariati implementati su misure spettroscopiche si rivelano essenziali per trattare i dati e agevolare la comprensione e il monitoraggio di processo.<br>Process analysis and monitoring has become essential in industry to ensure improvement of the process performances and to maintain a specific product quality. To this aim, spectroscopy represents an innovative tool that allows to overcome the issues encountered with conventional analytical techniques (e.g. gas chromatography), since it is fast and non-destructive and can give information about the chemical state of the process in real time. Nevertheless, due to the huge amount of information present in the collected data, the interpretation and information extraction is not a straightforward task. For this purpose, multivariate techniques significantly aid the treatment of the data and allow to infer information about the system analyzed.
In this thesis, four systems are investigated by means of spectroscopy to show the variety of problems that may arise when dealing with complex and highly informative data coming from different spectroscopic techniques. To this aim, different multivariate techniques are explored and their potentialities and limitations are shown: (i) Strategies based on Principal Component Analysis and Partial Least Squares Regression are suggested for an improved and more robust quality monitoring of liquid commercial detergents; (ii) Moving Window Principal Component Analysis is proposed for the monitoring of an evolving process like the crystallization of an Active Pharmaceutical Ingredient in order to detect the nucleation; (iii) Time Window Statistical Total Correlation Spectroscopy combined with Multivariate Curve Resolution are proposed to investigate the setting reaction of a cementing material; (iv) Multivariate Curve Resolution is employed to infer information from hyperspectral data about the dissolution of a surfactants paste.
Therefore, multivariate techniques applied to spectroscopic data demonstrate capable of achieving the following results:
a) in case of commercial detergents, they correctly classify observations that do not agree with the reference conditions. Moreover, the approach proposed is able to assess when the estimation of the compounds concentration cannot be considered accurate, this scenario may occur when the deviations of one compound is not taken into account during model calibration;
b) for the crystallization of the pharmaceutical ingredient, the nucleation is accurately detected;
c) spectra and concentration of the compounds involved in the setting reaction of a cementing material are estimated and time evolution of the process can be tracked;
d) the dissolution rate of the surfactants present in the paste is estimated.
As a result, multivariate methods applied to spectroscopic data reveal essential to treat data and aid process understanding and monitoring.
6
Hu, Cecilia X. Matthew C. Knitt. "A comparative analysis of multivariate statistical detection methods applied to syndromic surveillance." Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FHu.pdf.
Texte intégralRésumé :
Thesis (M.S. in Applied Science (Operations Research) )--Naval Postgraduate School, June 2007.<br>Thesis Advisor(s): Ronald D. Fricker. "June 2007." Includes bibliographical references (p. 71-72). Also available in print.
7
Knitt, Matthew C. "A comparative analysis of multivariate statistical detection methods applied to syndromic surveillance." Thesis, Monterey, California. Naval Postgraduate School, 2007. http://hdl.handle.net/10945/3417.
Texte intégralRésumé :
Biological terrorism is a threat to the security and well-being of the United States. It is critical to detect the presence of these attacks in a timely manner, in order to provide sufficient and effective responses to minimize or contain the damage inflicted. Syndromic surveillance is the process of monitoring public health-related data and applying statistical tests to determine the potential presence of a disease outbreak in the observed system. Our research involved a comparative analysis of two multivariate statistical methods, the multivariate CUSUM (MCUSUM) and the multivariate exponentially weighted moving average (MEWMA), both modified to look only for increases in disease incidence. While neither of these methods is currently in use in a biosurveillance system, they are among the most promising multivariate methods for this application. Our analysis was based on a series of simulations using synthetic syndromic surveillance data that mimics various types of background disease incidence and outbreaks. We found that, similar to results for the univariate CUSUM and EWMA, the directionally-sensitive MCUSUM and MEWMA perform very similarly.
8
Robson, Geoffrey. "Multiple outlier detection and cluster analysis of multivariate normal data." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53508.
Texte intégralRésumé :
Thesis (MscEng)--Stellenbosch University, 2003.<br>ENGLISH ABSTRACT: Outliers may be defined as observations that are sufficiently aberrant to arouse the
suspicion of the analyst as to their origin. They could be the result of human error, in
which case they should be corrected, but they may also be an interesting exception,
and this would deserve further investigation.
Identification of outliers typically consists of an informal inspection of a plot of
the data, but this is unreliable for dimensions greater than two. A formal procedure
for detecting outliers allows for consistency when classifying observations. It also
enables one to automate the detection of outliers by using computers.
The special case of univariate data is treated separately to introduce essential
concepts, and also because it may well be of interest in its own right. We then consider
techniques used for detecting multiple outliers in a multivariate normal sample,
and go on to explain how these may be generalized to include cluster analysis.
Multivariate outlier detection is based on the Minimum Covariance Determinant
(MCD) subset, and is therefore treated in detail. Exact bivariate algorithms were
refined and implemented, and the solutions were used to establish the performance
of the commonly used heuristic, Fast–MCD.<br>AFRIKAANSE OPSOMMING: Uitskieters word gedefinieer as waarnemings wat tot s´o ’n mate afwyk van die verwagte
gedrag dat die analis wantrouig is oor die oorsprong daarvan. Hierdie waarnemings
mag die resultaat wees van menslike foute, in welke geval dit reggestel moet
word. Dit mag egter ook ’n interressante verskynsel wees wat verdere ondersoek
benodig.
Die identifikasie van uitskieters word tipies informeel deur inspeksie vanaf ’n
grafiese voorstelling van die data uitgevoer, maar hierdie benadering is onbetroubaar
vir dimensies groter as twee. ’n Formele prosedure vir die bepaling van uitskieters
sal meer konsekwente klassifisering van steekproefdata tot gevolg hˆe. Dit gee ook
geleentheid vir effektiewe rekenaar implementering van die tegnieke.
Aanvanklik word die spesiale geval van eenveranderlike data behandel om noodsaaklike
begrippe bekend te stel, maar ook aangesien dit in eie reg ’n area van
groot belang is. Verder word tegnieke vir die identifikasie van verskeie uitskieters in
meerveranderlike, normaal verspreide data beskou. Daar word ook ondersoek hoe
hierdie idees veralgemeen kan word om tros analise in te sluit.
Die sogenaamde Minimum Covariance Determinant (MCD) subversameling is
fundamenteel vir die identifikasie van meerveranderlike uitskieters, en word daarom
in detail ondersoek. Deterministiese tweeveranderlike algoritmes is verfyn en ge¨ımplementeer,
en gebruik om die effektiwiteit van die algemeen gebruikte heuristiese algoritme,
Fast–MCD, te ondersoek.
9
Van, Deventer Petrus Jacobus Uys. "The applicability of discriminant analysis techniques on the multivariate normal and non-normal data types in marketing research." Master's thesis, University of Cape Town, 1985. http://hdl.handle.net/11427/4941.
Texte intégralRésumé :
Includes bibliography.<br>The purpose of the procedures described is to assign “objects” or "observations" in some optimum fashion to one of two or more populations. In routine banking a bank manager may wish to classify clients who wish to make loans as low or high credit risks on the basis of the elements of certain accounting statements. In such a case there are two definite distinct classes. Another investigation may be initiated to determine whether buying habits are different with respect to the categories: urban, sub-urban and rural clients. Note that in the first example the classes are determined before any sample of observations is investigated, i.e. the sample results do not influence the choice of groups. In the latter case one is trespassing on the terrain of cluster analysis.In the first case we have two types of problems, namely that of devising a classification rule from samples of already classified objects and that of imposing the classification scheme on the objects. The term "discrimination" refers to the process of deriving classification rules from samples of classified objects and the term "classification" refers to applying the rules to knew objects of unknown class. Although it is possible to convert raw data into more easily grasped forms like cartoon faces (Chernoff, 1973) this still represents the problem that any grouping or classification based on these diagrams is subjective.
10
Pasamontes, Fúnez Alberto. "Multivariate curve resolution applied to sequential injection data. Analysis of amoxicillin anda clavulanic acid." Doctoral thesis, Universitat Rovira i Virgili, 2006. http://hdl.handle.net/10803/9005.
Texte intégralRésumé :
El objetivo de esta tesis ha sido estudiar y desarrollar metodologias analíticas<br/>usando un sistema de inyección secuencial (SIA) con un espectrofotómetro de diodos en<br/>fila para obtener datos de segundo orden. Para tratar estos datos, las herramientas<br/>quimiométricas usadas han sido; resolución de curvas multivariante mediante mínimos<br/>cuadrados alternados (MCR-ALS) y otras técnicas relacionadas a ésta como el análisis<br/>de componentes principales (PCA) y SIMPLISMA. Además se han aplicado estrategias<br/>de diseño de experimentos para obtener las condiciones experimentales óptimas. Esta<br/>metodología se aplicó a la determinación de amoxicilina y ácido clavulánico en<br/>medicamentos.<br/>El primer capítulo de la tesis contiene una descripción de la amoxicilina y del ácido<br/>clavulánico, una explicación de los fundamentos teóricos tanto del sistema instrumental<br/>como de las herramientas quimiométricas usadas y por último, se describen los diseños<br/>de experimentos usados y la función de deseabilidad.<br/>En los dos siguientes capítulos, se muestran en forma de artículos científicos los<br/>trabajos experimentales realizados. En un primer artículo, se realizó una clasificación de<br/>los medicamentos dependiendo si se tenían interferentes o no, para posteriormente<br/>proponer el tipo de calibrado. Un paso previo a la diferenciación de los medicamentos,<br/>fue buscar una secuencia analítica que permitiera obtener un sistema en evolución. Esta<br/>etapa se llevó a cabo mediante un diseño de experimentos.<br/>En el segundo artículo, se determinó la cantidad de amoxicilina en los<br/>medicamentos que tenían interferentes y además no tenían zonas selectivas. Para llevar<br/>a cabo de forma correcta la etapa de calibración se realizó un estudio de una serie de<br/>parámetros asociados a MCR-ALS. En un tercer artículo se realizó la determinación<br/>simultánea del ácido clavulánico y de la amoxicilina que poseían unas características<br/>ácido-base y una sensibilidad espectral similar. Por tal de determinar simultáneamente<br/>ambos analitos se rediseñó todo el experimental. En el cuarto artículo se hizo una<br/>revisión bibliográfica de ambas técnicas a partir del año 2004 y se discutió el potencial de<br/>usar un sistema de inyección secuencial para generar datos de segundo orden.<br/>Con la experimentación realizada se comprobó que el paso clave en estas<br/>metodologias era obtener una buen sistema en evolución, es decir diseñar una buena<br/>secuencia analítica. Por lo que se profundizó en estrategias basadas en diseños de<br/>experimentos. En el quinto artículo, se estudiaron qué factores podían afectar a la<br/>secuencia analítica. También se propusieron respuestas que representaran de una forma<br/>cuantitativa una buena resolución. Se realizó un diseño Plackett-Burman con el objetivo<br/>de eliminar los factores no relevantes, para posteriormente modelar una superficie de<br/>respuesta a partir de los factores relevantes que permite visualizar las condiciones<br/>óptimas de la secuencia analítica.<br/>El inconveniente de utilizar la metodología de superficie de respuesta es que en<br/>los casos donde el número de factores relevantes sea superior a 3 o 4, el número de<br/>experiencias aumenta considerablemente. En estos casos, una técnica alternativa es<br/>simplex que dio lugar a un sexto artículo.<br/>El último capítulo de la tesis contiene las conclusiones. Como una conclusión<br/>general, se puede decir que la combinación de un sistema de inyección secuencial (SIA)<br/>y una herramienta quimiométrica como la resolución de curvas multivariante mediante<br/>mínimos cuadrados alternados (MCR-ALS) puede ser usado tanto para realizar un<br/>análisis cualitativo y cuantitativo ya que proporciona información de los perfiles de<br/>concentración y perfiles espectrales de las diferentes especies a estudio.<br>The objective of this thesis is to study and develop analytical methods to determine<br/>amoxicillin and clavulanic acid in pharmaceuticals using sequential injection analysis (SIA)<br/>with a diode-array spectrophotometric detector to obtain second-order data. To treat these<br/>data, the chemometric tool used was; multivariate curve resolution with alternating least<br/>squares (MCR-ALS) and the techniques involved in the resolution process are: principal<br/>analysis components (PCA) and simple-to-use interactive self-modelling mixture analysis<br/>(SIMPLISMA).<br/>The first chapter contains a brief description of the theoretical backgrounds that<br/>have been used during this thesis. We explain the characteristics and properties of<br/>amoxicillin and clavulanic acid, we describes the instrumental and the chemometric tools<br/>used and at the end, we introduce the experimental designs used and the desirability<br/>function.<br/>In the next two chapters contain the bulk of the work carried out for this thesis and<br/>incorporate papers published in journals. In the first paper, the pharmaceuticals were<br/>classified according to their selective zones in order to propose the type of calibration. In a<br/>previous step, the experimental work was conducted to find an analytical sequence that<br/>allows us to obtain an evolving system. This step was carried out using experimental<br/>design. In the second paper, the quantity of amoxicillin in the pharmaceuticals with<br/>interferents or without selective zones was determined. To carry out correctly the<br/>calibration step, we studied different conditions related to the MCR-ALS process.<br/>In the third paper, we propose the simultaneous determination of amoxicillin and<br/>clavulanic acid which they have the acid-base characteristics and spectral profile similar.<br/>To determine both analytes, a new analytical sequence was redesigned. In the fourth<br/>paper, we describe the state of the art of sequential injection analysis (SIA) and<br/>multivariate curve resolution with alternating least squares (MCR-ALS) by reviewing the<br/>bibliography since 2004. We discuss the potential of SIA for generating second-order<br/>data.<br/>In previous papers, we found that the most critical step in the development of<br/>analytical methods based on SIA and MCR-ALS was to obtain an analytical sequence that<br/>provides an evolving system. To resolve so, we developed the method of experimental<br/>design to obtain the optimal analytical sequence.<br/>In the forth paper, we studied all the factors and analysed how they affect to the<br/>analytical sequence. We also proposed responses to quantitatively represent a good<br/>resolution. Once these factors and responses were proposed, we used a Plackett-Burman<br/>design to remove the non-relevant factors and then modelled a response surface. In the<br/>maximum of response surface, the optimum conditions for the analytical sequence could<br/>be visualised. To transform several responses into a single response, we used the overall<br/>desirability function. In the sixth paper, we applied an alternative optimisation method<br/>knows as the simplex approach. We aimed to determine amoxicillin and clavulanic acid<br/>simultaneously when the number of factors and responses was higher than in the<br/>previous paper.<br/>The last chapter contains the conclusions of the thesis. In general, we conclude<br/>that a combined sequential injection analysis (SIA) with a multivariate detector (i.e. diode<br/>array spectrophotometer) and multivariate curve resolution with alternating least squares<br/>(MCR-ALS) can be used for both qualitative and quantitative analyses since, it provides<br/>concentration and spectra profiles for the different species of the sample.
11
Reising, Justin. "Function Space Tensor Decomposition and its Application in Sports Analytics." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3676.
Texte intégralRésumé :
Recent advancements in sports information and technology systems have ushered in a new age of applications of both supervised and unsupervised analytical techniques in the sports domain. These automated systems capture large volumes of data points about competitors during live competition. As a result, multi-relational analyses are gaining popularity in the field of Sports Analytics. We review two case studies of dimensionality reduction with Principal Component Analysis and latent factor analysis with Non-Negative Matrix Factorization applied in sports. Also, we provide a review of a framework for extending these techniques for higher order data structures. The primary scope of this thesis is to further extend the concept of tensor decomposition through the use of function spaces. In doing so, we address the limitations of PCA to vector and matrix representations and the CP-Decomposition to tensor representations. Lastly, we provide an application in the context of professional stock car racing.
12
Guamán, Novillo Ana Verónica. "Multivariate Signal Processing for Quantitative and Qualitative Analysis of Ion Mobility Spectrometry data, applied to Biomedical Applications and Food Related Applications." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/349210.
Texte intégralRésumé :
There are several applications where the measurement of VOC results to be useful, such as: toxic leaks, air quality measurements, explosive detection, monitoring of food and beverages quality, diagnosis of diseases, etc. Some of this applications claim for fast responses or even real time responses. In this context, there are few analytical techniques for performing gas phase analysis, among of them Ion Mobility Spectrometry (IMS).
IMS is a fast analytical device based on the time of flight of ions in a drift tube. The response of IMS lasts typically few seconds, but it can be even less than a second. This fast response has drifted its use towards novel applications, such as biomedical and food applications (bio-related applications). Nonetheless, it has also brought the need to analyze complex spectra with hundreds of compounds. In fact, tackling this disadvantage is the main focus of this thesis, where new algorithms for enhancing the IMS performance are investigated when are applied to bio-related applications.
Nonlinear behavior and charge competitions of IMS responses are important issues that need to be addressed. Both effects have a direct impact in the IMS spectra interpretation —especially when real dataset are studied. Additionally, the use of univariate spectra analysis, where peaks information is extracted manually, becomes unfeasible in bio-related applications. In this context, this work introduces multivariate methodologies focused on quantitative and qualitative analysis.
In the case of quantitative analysis, calibration models were built using univariate methodology, Partial Leas Squares (PLS) and Multivariate Curve Resolution techniques (MCR). The quantitative analysis aims tackling the main issues of IMS such as non linearities and mixture effect. Definitely, univariate techniques provides poor or overoptimistic results that minimize the impact of the IMS use. The results show a really improvement on the performance when multivariate techniques were used. Regarding the results between MCR and PLS, the main difference is the interpretability that offers MCR.
In the case of qualitative analysis, two different approaches were planned for building models for classes' discrimination. The first approach consisted on building a model through principal component analysis and linear discriminant analysis, besides of using robust cross validation methodology for obtaining reliable results. This methodology were implemented in samples of wine, where main motivation was found discrimination regarding to their origin. The results were fully satisfactory because the model was able to separate four groups with a high accuracy rate. The second approach involves the use of Multivarite Curve Resolution — Lasso algorithm for extracting pure components of samples from rats' breath and then use a feature selection technique for obtaining the most representative features subset. In this case, the objective of the application was to find a model that discriminate rats with sepsis from control rats. The results shows there were few pure components of IMS that generate a discriminatory model that means there are specific compounds in the breath linked with the disease.
Summarizing, the following proposal has as main objective resolving open issues in stand-alone IMS that are applied to the analysis of bio-related applications. Two major investigation lines were proposed in this thesis: (i) qualitative analysis and (ii) quantitative analysis. The qualitative analysis covers pre-processing algorithms and the developing of new methodologies for building models in bio-related applications. The quantitative analysis are focused on highlighting the importance of the use of multivariate techniques instead of univariate techniques. In order to reach the objectives of this thesis, a set of datasets were created, which are detailed on the content of this thesis. The results and main conclusions are deeply explained in the extended proposal.<br>El objetivo de esta tesis es el desarrollo de nuevas metodologías en el procesado de señal multivariante en espectros IMS.
En este trabajo se ha realizado una comparación entre tres espectrómetros IMS. Esta labor comparativa, mediante procesado multivariante, es prácticamente inédita en este ámbito. En este caso se realizó un estudio con 3 aminas y se determinó el límite de detección. Los resultados mostraron que los 3 espectrómetros tuvieron un rendimiento similar, a pesar de que sus condiciones de operación son distintas.
Se propuso una técnica específica para eliminar ruido de baja frecuencia acoplado al espectro de IMS. Se observó que utilizar PCA o ICA (métodos multivariantes) mejora notablemente la relación señal ruido si se compara con las técnicas convencionales.
Se ha estudiado el alineamiento de los espectros y se han propuesto soluciones basadas en los diferentes métodos del estado del arte. Se ha evidenciado que incluir compuestos de referencia para garantizar que el proceso de alineamiento es el adecuado es ventajoso. En el caso de que esto no fuese posible se aconseja realizar el alineamiento por etapas, primero un alineamiento en una misma muestra, y luego entre muestras.
Se realizaron modelos cualitativos para diferenciar o discriminar clases a partir de medidas de IMS. Se propusieron dos modelos multivariantes con técnicas de validación cruzada. Los resultados obtenidos muestran el gran potencial de IMS en este sentido.
Se evaluó el rendimiento cuantitativo de los IMS al utilizar métodos multivariantes y fueron comparados con métodos univariantes habituales en el ámbito de IMS. De los resultados obtenidos se observó que los modelos univariantes no son capaces de resolver comportamientos típicos de IMS como son el comportamiento no lineal y el efecto en mezclas. En este sentido las técnicas multivariantes mostraron mejores prestaciones. Se comparó la utilización de técnicas multivariantes que proyectan los datos en un nuevo subespacio como lo es PLS con técnicas de deconvolución como lo es MCR en sus dos versiones ALS y Lasso. Los resultados obtenidos fueron bastante similares, sin embargo MCR ofrece una ventaja importante ya que permite interpretar de mejor manera los resultados.
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Rosinski, Bernard J. "Roman Catholic seminary survival (1968-1983) : a multivariate statistical analysis of the CARA seminary directories." Virtual Press, 1985. http://liblink.bsu.edu/uhtbin/catkey/434859.
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The purpose of the study was to provide a predictive account for a nearly 60 percent decrease in the number of Catholic seminaries extant in 1968. Standards for seminary operations published by the Catholic Bishops in 1968 and 1971 were hypothesized to have no predictive relationship with seminary survival. The population consisted of all qualified Catholic seminaries in the United States. Cell group arrangements, obtained from the intersection of seminary survival with seminary academic levels, served as criterion composites with a variety of seminary measures serving as predictor composites in four differentiated multiple discriminant analyses. Survival alone served as criterion with regression analysis.The annual CARA Seminary Directory served as database. Values for more than seventy measures were obtained for use with univariate hypotheses tests from the CARA Seminary Directory. The level of probability set for rejection of the null hypotheses was .05.Findings1.Student body size, number of doctors on faculty, number of bachelors on faculty, and diocesan Catholic population were among the successful stepwise predictors of seminary survival.2.Size of faculty, size of administration, total number of priests, articulation pattern, state approval, and number of professional memberships were among the unsuccessful stepwise predictors of seminary survival.3. Generally, hypothesized variables were unsuccessful blockwise predictors of survival.4.Seventeen significant discrimant functions were found in the four discriminant analyses; eight functions reduced to two successful predictors of seminary survival by seminary level for initial and terminal data sets: (1) faculty qualification, and (2) commitment to run a school.Conclusions1.Multivariate predictors of seminary survival based on 1968 data differed from predictors based on terminal data for the most part.2. Individual norms and standards proposed by the Catholic Bishops had an unanticipated joint effect upon seminary survival.3. For the most part, closure or amalgamation of seminaries could not be predicted by failure to fulfill particular norms or standards proposed by the Catholic Bishops.
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Lai, Di. "Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization /." Online version of thesis, 2009. http://hdl.handle.net/1850/11367.
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Piovesan, Pamela. "Validação cruzada com correção de autovalores e regressão isotônica nos modelos AMMI." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-16102007-113618/.
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Neste trabalho apresenta-se a aplicação dos modelos AMMI para um estudo detalhado do efeito das interações entre genótipos e ambientes em experimentos multiambientais. Através da decomposição da soma de quadrados dessas interações, busca-se selecionar o número de termos que explicam essa interação, descartando o ruído presente na mesma. Há duas maneiras para a escolha desses termos: validação cruzada e teste de hipóteses. O foco será na validação cruzada pela vantagem de ser um critério "preditivo" de avaliação. São apresentados dois métodos de validação cruzada, métodos esses esboçados por Eastment e Krzanowski (1982) e Gabriel (2002). Esses métodos utilizam a decomposição por valores singulares para obter os autovalores referentes à matriz de interações, cuja soma de quadrados nos dá exatamente a soma de quadrados da interação. Como esses autovalores são superestimados ou subestimados (ARAÚJO; DIAS, 2002), essas técnicas de validação serão aperfeiçoadas através da correção desses autovalores e, para reordená-los, será utilizada a regressão isotônica. Será realizado um estudo comparativo entre esses métodos, através de dados reais.<br>This paper presents the application of AMMI models for a thorough study about the effect of the interaction between genotypes and environments in multi-environments experiments. Through the decomposition of the sum of squares of these interactions, one searches to select the number of terms that explains this interaction, discarding its noise in. There are two ways for choosing these terms: cross-validation and hypotheses test. The focus will be on the crossvalidation for its advantage of being one prediction criterion of evaluation. Two methods of cross-validation are presented , both outlined by Eastment and Krzanowski (1982) and Gabriel (2002). These methods use the decomposition by singular values in order to obtain eigenvalues referred to the matrix of interactions, whose sum of squares accurately gives us the sum of squares of the interation. As these eigenvalues either over- or underestimated (ARAÚJO; DIAS, 2002), these techniques of validation will be improved through the correction of these eigenvalues and, in order to rearrange them, isotonic regression will be used . A comparative study between these methods through real data will be carried out.
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Miller, Lisa V. "Multivariate statistical analysis of seismic data applied to the delineation of reservoir facies in the Birkhead Formation, Sturt Field, Lake Hope Block /." Title page, contents and abstract only, 1994. http://web4.library.adelaide.edu.au/theses/09SM/09smm648.pdf.
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SILVA, Adriano Victor Lopes da. "Alternativas e comparações de modelos lineares para estimativa da biomassa verde de Bambusa vulgaris na existência de multicolinearidade." Universidade Federal Rural de Pernambuco, 2008. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4454.
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Previous issue date: 2008-02-26<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES<br>The objective of this work was to use univariate and multivariate statistical methods on selection of independent variables, in the mathematical linear models, to estimate the green biomass of the main bamboo rod, bambusa vulgaris, pursuing time and cost reduction without loss of precision. The data came from an experiment carried out for the Agroindustrial Excelsior S. A. (Agrimex) company located in the city of Goiana – PE.Quantified by its green biomass weight,450 bamboo rods were used and 4 independent variables measured in the rod. Initially, the effect of the multicollinearity could be verified through the correlation matrix of the independent variables and the varience inflation factors.To select the independent variables two methods were used: Stepwise and K component retention. The alternatives used were component regression and Ridge regression. In general, in only one situation the variable selection methods behave adequately while multicollinearity is present among the independent variables, that is the multivaried method of retention K=3 component for the covariate matrix, model of Spurr. The estimative of alternative methods showed similar responses, however, the principal component regression yields the best results.<br>O objetivo deste trabalho foi utilizar métodos estatísticos univariado e multivariado na seleção de variáveis independentes, em modelos matemáticos lineares para a estimativa da biomassa verde da haste principal do bambu, Bambusa vulgaris, visando reduzir tempo e custo sem perda de precisão, além de empregar alternativas para estimação na existência de multicolinearidade. Os dados foram provenientes de um experimento conduzido pela empresa Agroindustrial Excelsior S. A. (Agrimex) localizada no Engenho Itapirema na cidade de Goiana – PE. Foram utilizadas 450 hastes de bambu, que tiveram sua biomassa verde quantificada através do peso e 4 variáveis independentes medidas na mesma haste. Inicialmente, verificou-se a existência da multicolinearidade por meio da matriz de correlação das variáveis independentes e pelo fator de inflação da variância. Para seleção das variáveis independentes foram utilizados os métodos:Stepwise e Retenção por K componentes. As alternativas utilizadas foram a Regressão com os componentes principais e Regressão Ridge. No geral, em apenas uma situação os métodos de seleção de variáveis se comportam adequadamente na existência de multicolinearidade entre as variáveis explicativas, exatamente o método multivariado de retenção por K=3 componente pela matriz de covariância, modelo de Spurr. Os métodos alternativos de estimação conduzem respostas semelhantes, mesmo que possuindo estruturas diferentes, no entanto, a regressão com os componentes principais obteve os melhores resultados.
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Rivers, Derick Lorenzo. "Dynamic Bayesian Approaches to the Statistical Calibration Problem." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3599.
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The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used to monitor the evolution of the measures, thus introducing a dynamic approach to statistical calibration. The research presented employs the use of Bayesian methodology to perform statistical calibration. The DLM framework is used to capture the time-varying parameters that may be changing or drifting over time. Dynamic based approaches to the linear, nonlinear, and multivariate calibration problem are presented in this dissertation. Simulation studies are conducted where the dynamic models are compared to some well known "static'" calibration approaches in the literature from both the frequentist and Bayesian perspectives. Applications to microwave radiometry are given.
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Eisman, Elyktra. "GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2261.
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Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.
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Olid, Pilar. "Making Models with Bayes." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/593.
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Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects the model. We will also make a comparison between the Bayesian approach and the traditional frequentist approach to data analyses.
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Yuan, Qingcong. "INFORMATIONAL INDEX AND ITS APPLICATIONS IN HIGH DIMENSIONAL DATA." UKnowledge, 2017. http://uknowledge.uky.edu/statistics_etds/28.
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We introduce a new class of measures for testing independence between two random vectors, which uses expected difference of conditional and marginal characteristic functions. By choosing a particular weight function in the class, we propose a new index for measuring independence and study its property. Two empirical versions are developed, their properties, asymptotics, connection with existing measures and applications are discussed. Implementation and Monte Carlo results are also presented.
We propose a two-stage sufficient variable selections method based on the new index to deal with large p small n data. The method does not require model specification and especially focuses on categorical response. Our approach always improves other typical screening approaches which only use marginal relation. Numerical studies are provided to demonstrate the advantages of the method.
We introduce a novel approach to sufficient dimension reduction problems using the new measure. The proposed method requires very mild conditions on the predictors, estimates the central subspace effectively and is especially useful when response is categorical. It keeps the model-free advantage without estimating link function. Under regularity conditions, root-n consistency and asymptotic normality are established. The proposed method is very competitive and robust comparing to existing dimension reduction methods through simulations results.
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Moradi, Rekabdarkolaee Hossein. "Dimension Reduction and Variable Selection." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4633.
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High-dimensional data are becoming increasingly available as data collection technology advances. Over the last decade, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics, signal processing, and environmental studies. Statistical techniques such as dimension reduction and variable selection play important roles in high dimensional data analysis. Sufficient dimension reduction provides a way to find the reduced space of the original space without a parametric model. This method has been widely applied in many scientific fields such as genetics, brain imaging analysis, econometrics, environmental sciences, etc. in recent years.
In this dissertation, we worked on three projects. The first one combines local modal regression and Minimum Average Variance Estimation (MAVE) to introduce a robust dimension reduction approach. In addition to being robust to outliers or heavy-tailed distribution, our proposed method has the same convergence rate as the original MAVE. Furthermore, we combine local modal base MAVE with a $L_1$ penalty to select informative covariates in a regression setting. This new approach can exhaustively estimate directions in the regression mean function and select informative covariates simultaneously, while being robust to the existence of possible outliers in the dependent variable. The second project develops sparse adaptive MAVE (saMAVE). SaMAVE has advantages over adaptive LASSO because it extends adaptive LASSO to multi-dimensional and nonlinear settings, without any model assumption, and has advantages over sparse inverse dimension reduction methods in that it does not require any particular probability distribution on \textbf{X}. In addition, saMAVE can exhaustively estimate the dimensions in the conditional mean function. The third project extends the envelope method to multivariate spatial data. The envelope technique is a new version of the classical multivariate linear model. The estimator from envelope asymptotically has less variation compare to the Maximum Likelihood Estimator (MLE). The current envelope methodology is for independent observations. While the assumption of independence is convenient, this does not address the additional complication associated with a spatial correlation. This work extends the idea of the envelope method to cases where independence is an unreasonable assumption, specifically multivariate data from spatially correlated process. This novel approach provides estimates for the parameters of interest with smaller variance compared to maximum likelihood estimator while still being able to capture the spatial structure in the data.
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Oketch, Tobias O. "Performance of Imputation Algorithms on Artificially Produced Missing at Random Data." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3217.
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Missing data is one of the challenges we are facing today in modeling valid statistical models. It reduces the representativeness of the data samples. Hence, population estimates, and model parameters estimated from such data are likely to be biased.
However, the missing data problem is an area under study, and alternative better statistical procedures have been presented to mitigate its shortcomings. In this paper, we review causes of missing data, and various methods of handling missing data. Our main focus is evaluating various multiple imputation (MI) methods from the multiple imputation of chained equation (MICE) package in the statistical software R. We assess how these MI methods perform with different percentages of missing data. A multiple regression model was fit on the imputed data sets and the complete data set. Statistical comparisons of the regression coefficients are made between the models using the imputed data and the complete data.
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Zaldivar, Cynthia. "On the Performance of some Poisson Ridge Regression Estimators." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3669.
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Multiple regression models play an important role in analyzing and making predictions about data. Prediction accuracy becomes lower when two or more explanatory variables in the model are highly correlated. One solution is to use ridge regression. The purpose of this thesis is to study the performance of available ridge regression estimators for Poisson regression models in the presence of moderately to highly correlated variables. As performance criteria, we use mean square error (MSE), mean absolute percentage error (MAPE), and percentage of times the maximum likelihood (ML) estimator produces a higher MSE than the ridge regression estimator. A Monte Carlo simulation study was conducted to compare performance of the estimators under three experimental conditions: correlation, sample size, and intercept. It is evident from simulation results that all ridge estimators performed better than the ML estimator. We proposed new estimators based on the results, which performed very well compared to the original estimators. Finally, the estimators are illustrated using data on recreational habits.
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Papageorgiou, Theofania. "Image analysis and multivariate morphometrics as means of distingushing between apple varieties and clones." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267281.
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Nunes, Giovani Cavalcanti. "Design and analysis of multivariable predictive control applied to an oil-water-gas separator a polynomial approach /." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp1585/Phd.pdf.
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Thesis (Ph. D.)--University of Florida, 2001.<br>Title from first page of PDF file. Document formatted into pages; contains viii, 118 p.; also contains graphics. Vita. Includes bibliographical references (p. 115-117).
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Hassan, Mahmoud. "Analysis of the propagation of uterine electrical activity applied to predict preterm labor : prédiction de menaces d'accouchement prématuré." Compiègne, 2011. http://www.theses.fr/2011COMP1948.
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Les contractions utérines sont contrôlées par deux phénomènes physiologiques: l'excitabilité cellulaire et la propagation de l'activité électrique utérine probablement liées aux hautes et basses fréquences de l'electrohysterograme (EHG) respectivement. Toutes les études précédentes ont porté sur l'extraction de paramètres de la partie haute fréquence et n'ont pas montré un potentiel satisfait pour l'application clinique. L'objectif de cette thèse est l'analyse de propagation de l'EHG pendant la grossesse et le travail dans la vue de l'extraction des outils pour une application clinique. Une des nouveautés de la thèse est l'enregistrement multicanaux à l'aide d'une matrice d'électrodes 4x4 posée sur l'abdomen de la femme. Analyse monovariés visait à étudier les caractéristiques non linéaires des signaux EHG, analyses bivariées et multivariées ont été effectuées pour analyser la propagation des signaux EHG par la détection de la connectivité entre les signaux. Une augmentation de la non-linéarité associée par une synchronisation en amplitude et de désynchronisation en phase a été détectée. Les résultats indiquent plus de propagation au cours du travail que la grossesse et une augmentation de cette propagation avec les semaines de gestations. Les résultats montrent le potentiel élevé de paramètres de propagation dans le point de vue clinique tel que la détection du travail et de prédiction du travail prématuré. Finalement, nous avons proposé une nouvelle combinaison entre Séparation Aveugles de Sources et la Décomposition en Modes Empiriques pour débruiter les signaux EHG monopolaires comme un moyen possible d'augmenter le taux de classification de signaux grossesse et l'accouchement<br>Uterine contractions are essentially controlled by two physiological phenomena: cell excitability and propagation of uterine electrical activity probably related to high and low frequencies of uterine electromyogram, called electrohysterogram -EHG-, respectively. All previous studies have been focused on extracting parameters from the high frequency part and did not show a satisfied potential for clinical application. The objective of this thesis is the analysis of the propagation EHG signals of during pregnancy and labor in the view of extracting tool for clinical application. A novelty of our thesis is the multichannel recordings by using 4x4 electrodes matrix posed on the woman abdomen. Monovariate analysis was aimed to investigate the nonlinear characteristics of EHG signals. Bivariate and multivariate analyses have been done to analyze the propagation of the EHG signals by detecting the connectivity between the signals. An increase of the nonlinearity associated by amplitude synchronization and phase desynchronization were detected. Results indicate a highest EHG propagation during labor than pregnancy and an increase of this propagation with the week of gestations. The results show the high potential of propagation’s parameters in clinical point of view such as labor detection and then preterm labor prediction. We proposed novel combination of Blind Source Separation and empirical mode decomposition to denoise monopolar EHG as a possible way to increase the classification rate of pregnancy and labor
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Taconeli, Cesar Augusto. "Árvores de classificação multivariadas fundamentadas em coeficientes de dissimilaridade e entropia." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-15102008-082243/.
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A análise estatística de grandes bancos de dados requer a utilização de metodologias flexíveis, capazes de produzir resultados esclarecedores e facilmente compreensíveis frente a dificuldades como a presença de números elevados de variáveis, diferentes graus de associações entre as mesmas e dados ausentes. A construção de árvores de classificação e regressão proporciona a modelagem de uma variável resposta, categorizada ou numérica, com base em um conjunto de covariáveis, sem esbarrar nas dificuldades mencionadas. A extensão multivariada de técnicas de classificação e regressão por árvores visa permitir a análise conjunta de duas ou mais variáveis respostas. Embora seja objeto de estudos recentes, a proposição de técnicas multivariadas de classificação e regressão por árvores tem sido verificada de maneira mais acentuada para situações em que se dispõe de múltiplas variáveis respostas numéricas. Propõemse, neste trabalho, novas alternativas para a construção de árvores de classificação multivariadas, visando analisar múltiplas variáveis respostas categorizadas. Tais alternativas baseiam-se em medidas de dissimilaridade e entropia. Por meio de um estudo de simulação, verificou-se o efeito das correlações e entropias das variáveis no desempenho das metodologias propostas (os resultados são melhores quanto maiores as entropias e correlações das variáveis sob estudo). A análise de dados de consumo de álcool e fumo dos habitantes do município de Botucatu-SP complementa o presente estudo, evidenciando, dentre outras coisas, que fatores como o grau de escolaridade, a ocupação profissional e a possibilidade de compartilhar problemas com amigos têm influência sobre os consumos de álcool e fumo dos habitantes.<br>The statistical analysis of large datasets requires the use of flexible methodologies, that can provide insight and understanding even in the presence of difficulties such as large numbers of variables having variable levels of association between themselves, and missing data. The construction of classification and regression trees allows for modeling of a categorical or numerical response variable as a function a set of covariates, while bypassing many of the cited difficulties. Multivariate trees extend classification and regression techniques to allow for joint analysis of two or more response variables. In recent studies, application of multivariate classification and regression techniques has been most common in situations involving numerical response variables. In this work we propose alternatives for constructing multivariate classification trees for multiple categorized response variables. Such alternatives are based on dissimilarity and entropy measures. A simulation study was used to examine the effect of variable correlations and entropies on the performance of the proposed methodology (results are better for high correlations and entropies). Analysis of data on alcohol consumption and smoking among inhabitants from Botucatu (SP) complements the analysis by showing that factors as the education level, daily occupation and possibility of sharing problems with friends have an influence on the alcohol consumption and smoking.
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Junior, Rafael Paiva Nunes. "Multivariate statistics applied to microplastic analysis techniques." Master's thesis, 2021. https://hdl.handle.net/10216/139478.
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鍾立人. "Multivariate Principle Component Analysis Applied in Distillation Column." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/61387910511759122615.
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碩士<br>中原大學<br>化學工程學系<br>87<br>The amount of data that we collect and store in modern chemical industrial plants with well-equipped computerized measurement devices has become quite large, and the volume of acquired data often exceeds our capability to detect whether the process is normal. If the abnormal event cannot be identified in time, the malfunction would cause monetary loss, plant shutdown, or even significant impacts on personnel safety.
In this research, statistical process control, including single variable and multivariable methods, is discussed. Shewhart Chart, CUSUM and EWMA for Single variable analysis are used here; PCA (Principal Component Analysis) for multivariable is applied here. Extensive testing and comparison on mathematical models and real experimental data from the distillation column are made to demonstrate the pros and cons. Temperature measurement instead of product concentration is used to detect the product quality in the distillation column. In our analysis, although the single variable statistical analysis is simple and easy, it is not appropriate for the multivariable system when collinearities occur, because it may lead to the false detection. This is particularly true in the most of chemical industrial processes. The effectiveness and excellence of the PCA technique are demonstrated from the testing examples.
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Szkudlarek, Cheryl Ann. "Multivariate Statistical Methods Applied to the Analysis of Trace Evidence." 2013. http://hdl.handle.net/1805/3456.
Texte intégralRésumé :
Indiana University-Purdue University Indianapolis (IUPUI)<br>The aim of this study was to use multivariate statistical techniques to: (1) determine the reproducibility of fiber evidence analyzed by MSP, (2) determine whether XRF is an appropriate technique for forensic tape analysis, and (3) determine if DART/MS is an appropriate technique for forensic tape analysis. This was achieved by employing several multivariate statistical techniques including agglomerative hierarchical clustering, principal component analysis, discriminant analysis, and analysis of variance. First, twelve dyed textile fibers were analyzed by UV-Visible MSP. This analysis included an inter-laboratory study, external validations, differing preprocessing techniques, and color coordinates. The inter-laboratory study showed no statistically significant difference between the different instruments. The external validations had overall acceptable results. Using first derivatives as a preprocessing technique and color coordinates to define color did not result in any additional information. Next, the tape backings of thirty-three brands were analyzed by XRF. After chemometric analysis it was concluded that the 3M tapes with black adhesive can be classified by brand except for Super 33+ (Cold Weather) and Super 88. The colorless adhesive tapes were separated into two large groups which were correlated with the presence of aluminosilicate filler. Overall, no additional discrimination was seen by using XRF compared to the traditional instrumentation for tape analysis previously published. Lastly, the backings of eighty-nine brands of tape were analyzed by DART/MS. The analysis of the black adhesive tapes showed that again discrimination between brands is possible except for Super 33+ and Super 88. However, now Tartan and Temflex have become indistinguishable. The colorless adhesive tapes again were more or less indistinguishable from one another with the exception of Tuff Hand Tool, Qualpack, and a roll of 3M Tartan, which were found to be unique. It cannot be determined if additional discrimination was achieved with DART/MS because the multivariate statistical techniques have not been applied to the other instrumental techniques used during tape analysis.
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Denis, Daniel J. "The rise of applied multivariate statistics : the consequences of correlation /." 2004. http://wwwlib.umi.com/cr/yorku/fullcit?pNQ99160.
Texte intégralRésumé :
Thesis (Ph.D.)--York University, 2004. Graduate Programme in Psychology.<br>Typescript. Includes bibliographical references (leaves 182-192). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ99160
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Dayman, Kenneth Joseph. "Multivariate analysis applied to the characterization of spent nuclear fuel." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5875.
Texte intégralRésumé :
The Multi-Isotope Process Monitor is being developed at Pacific Northwest National Laboratory as a method to verify the process conditions within a nuclear fuel reprocessing facility using the gamma spectra of various process streams. The technique uses multivariate analysis techniques such as principal component analysis and partial least squares regression applied to gamma spectra collected of a process stream in order to classify the contents as belonging to a normal versus off-normal chemistry process. This approach to process monitoring is designed to function automatically, nondestructively, and in near real-time.
To extend the Multi-Isotope Process Monitor, an analysis method to char- acterize spent nuclear fuel based on the reactor of origin, either pressurized or boiling water reactor, and burnup of the fuel using nuclide concentrations as input data has been developed. While the Multi-Isotope Process Monitor uses gamma spectra as input data, nuclide activities were used in this work as an initial step before
Nuclide composition information was generated using ORIGEN-ARP for different fuel assembly types, initial 235U enrichments, burnup values, and cooling times. This data was used to train, tune, and test several multivariate analysis algorithms in order to compare their performance and identify the technique most suited for the analysis. To perform the classification based on reactor type, four methods were considered: k-nearest neighbors, linear and quadratic discriminant analysis, and support vector machines. Each method was optimized, and its performance on a validation set was used to determine the best method for classifying the fuel reactor class. Partial least squares was used to make burnup predictions. Three models were generated and tested: one trained on all the data, one trained for just pressurized water reactors, and one trained for boiling water reactors.
Quadratic discriminant analysis was chosen as the best classifier of reactor class because of its simplicity and its potential to be extended to classify spent nuclear fuel’s fuel assembly type, i.e, more specific classes, using nuclide concentrations as input data. In the case of predicting the burnup of spent fuel using partial least squares, it was determined that making reactor-specific partial least squares models, one trained for pressurized water reactors and one trained for boiling water reactors, performed better than a single, general model that was trained for all light water reactors. Thus, the the classifier, regression algorithm, and all the necessary intermediate data processing steps were combined into a single analysis method and implemented as a Matlab function called “burnup.”
This function was used to test the analysis routine on an additional set of data generated in ORIGEN-ARP. This dataset included samples with parameters that were not represented in the development data in order to ascertain the analysis method’s ability to analyze data for which it has not been explicitly trained. The algorithm was able to achieve perfect binary classification of the reactor as being a pressurized or boiling water reactor on the dataset and made burnup predictions with an average error of 0.0297%.<br>text
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Hassoulas, Vasilios. "General multivariate approximation techniques applied to the finite element method." Thesis, 2015. http://hdl.handle.net/10539/16739.
Texte intégral
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Tavares, Jorge Manuel Santos Freire. "Exploraty Multivariate Statistical Methods Applied to Pharmaceutical Industry CRM Data." Master's thesis, 2008. http://hdl.handle.net/10362/8218.
Texte intégralRésumé :
Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação<br>An analysis of the current CRM systems in the Pharmaceutical Industry, the way the
pharmaceutical companies developed them and a comparison between Europe and United States
was done in this study. Overall the CRM in the pharmaceutical industry is far-behind, when compared with other business areas, like consumer goods, finance (banking) or insurance companies, being pharmaceutical CRM specifically less developed in Europe when compared to United States.
One of the big obstacles for the success of CRM in the pharmaceutical industry is the poor analytics applied to the current CRM programs. Improving Sales and Marketing Effectiveness by apllying, multivariate exploratory statistical methods, specifically Factor Analysis and
Clustering into pharmaceutical CRM data from a Portuguese pharmaceutical company was the
main goal of this thesis. Their overall usefulness when applied to the business was
demonstrated, and specifically in relation to the cluster methods, SOMs outperformed the
hierarchical methods by producing a more meaningful business solution.
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Hsieh, Tung Liang, and 謝東良. "A Study of Multivariate Analysis applied on the Judgment of Debris Flow Hazards." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/21487398339627860691.
Texte intégral
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Reichard, Eric Jonathan. "Chemometrics applied to the discrimination of synthetic fibers by microspectrophotometry." 2014. http://hdl.handle.net/1805/3795.
Texte intégralRésumé :
Indiana University-Purdue University Indianapolis (IUPUI)<br>Microspectrophotometry is a quick, accurate, and reproducible method to compare colored fibers for forensic purposes. The use of chemometric techniques applied to spectroscopic data can provide valuable discriminatory information especially when looking at a complex dataset. Differentiating a group of samples by employing chemometric analysis increases the evidential value of fiber comparisons by decreasing the probability of false association. The aims of this research were to (1) evaluate the chemometric procedure on a data set consisting of blue acrylic fibers and (2) accurately discriminate between yellow polyester fibers with the same dye composition but different dye loadings along with introducing a multivariate calibration approach to determine the dye concentration of fibers. In the first study, background subtracted and normalized visible spectra from eleven blue acrylic exemplars dyed with varying compositions of dyes were discriminated from one another using agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). AHC and PCA results agreed showing similar spectra clustering close to one another. DA analysis indicated a total classification accuracy of approximately 93% with only two of the eleven exemplars confused with one another. This was expected because two exemplars consisted of the same dye compositions. An external validation of the data set was performed and showed consistent results, which validated the model produced from the training set. In the second study, background subtracted and normalized visible spectra from ten yellow polyester exemplars dyed with different concentrations of the same dye ranging from 0.1-3.5% (w/w), were analyzed by the same techniques. Three classes of fibers with a classification accuracy of approximately 96% were found representing low, medium, and high dye loadings. Exemplars with similar dye loadings were able to be readily discriminated in some cases based on a classification accuracy of 90% or higher and a receiver operating characteristic area under the curve score of 0.9 or greater. Calibration curves based upon a proximity matrix of dye loadings between 0.1-0.75% (w/w) were developed that provided better accuracy and precision to that of a traditional approach.
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Askew, Andrew Warren. "A comparison of multivariate data analysis techniques as applied to the identification of electrons and tau leptons." Thesis, 2001. http://hdl.handle.net/1911/17401.
Texte intégralRésumé :
This thesis compares the performance of Probability Density Estimation and Neural Networks as applied to the identification of tau leptons and electrons at the DO detector for Run II. The theory behind each method of multivariate analysis is briefly described. The efficiencies of each of the methods are compared from analysis of Monte Carlo data samples, and optimal choices for the discrimination between signal and background are made.
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"Supervised and Ensemble Classification of Multivariate Functional Data: Applications to Lupus Diagnosis." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.50109.
Texte intégralRésumé :
abstract: This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of the temperature domain that are critical for population discrimination. The undertaken analyses suggest that derivate-based information contributes significantly in improved classification performance relative to recently published studies on SLE plasma thermograms.<br>Dissertation/Thesis<br>Doctoral Dissertation Applied Mathematics 2018
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Miller, Lisa V. "Multivariate statistical analysis of seismic data applied to the delineation of reservoir facies in the Birkhead Formation, Sturt Field, Lake Hope Block." Thesis, 1994. http://hdl.handle.net/2440/122465.
Texte intégralRésumé :
The delineation of seismically thin reservoirs is a common problem in the exploration for and development of hydrocarbon reserves in many basins. Reservoirs which are thinner than a quarter of the dominant wavelength of the incident wavelet cannot be resolved using time difference measurements made on the seismic waveform. The "thin bed" can only be inferred by waveform analysis. Most "thin bed" studies involve the examination of only a few attributes, for example amplitude and./or frequency of the seismic data. This thesis however, applies multivariate statistics, which can deal quantitatively with as many attributes as the user cares to define. The target chosen for this study was the thin reservoir sandstones within the Birkhead Formation of the Sturt Field in the Eromanga Basin, South Australia. These sands form combination structural-stratigraphic traps which are very difficult exploration targets. Before the statistical analysis, l-D and 1.5-D modelling was performed and this demonstrated that some of the thirty-four parameters measured from the Birkhead seismic signature should show measurable variations between the reservoir and non-reservoir facies. The parameters were measured from the trace amplitude, autocorrelation, power spectrum, complex amplitude and instantaneous frequency. Modelling also provided an understanding of the way in which geologic variations affected the seismic attributes from which the parameters were calculated. The next step was applied to both the model and field data. It was modified from the procedure reported by Dumay and Fournier (1988) and involved selecting a set of reference traces around wells where the geology was known. Each set of traces represented either the reservoir facies or a non-reservoir facies. Principle Component Analysis (PCA) was then applied to see if the seismic attribute differences could be detected statistically. Many of the thirty-four variables showed some interdependence and were therefore omitted. A total of six significant variables remained in the analysis. For the model data there were two parameters from the autocorrelation, one from the power spectrum and two from the complex amplitude. For the real data these numbers were three, one and two respectively. Discrimination between the reservoir and non-reservoir environments proved possible for the model and real data. The discriminant functions defined in the previous step were used to classify traces away from the well control where the geology, (in the real data) is unknown. Classification of the model data showed that reservoir sands could be predicted down to a minimum thickness of l5ft. Classification of the real data provided a map of the predicted reservoir facies distribution for the whole study area. At the conclusion of this study, it is not yet possible to assert that the method is reliable due to a lack of drilling in critical locations to test its predictions. However, modelling provides strong evidence that the channel sand facies is statistically distinct from the non-reservoir facies. The similarity between seismic attribute patterns for the real data and model data adds confidence in the use of this multivariate statistical method as an interpretative tool.<br>Thesis (M.Sc.)--University of Adelaide, Dept. of Geology and Geophysics, 1994
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Austin, Elizabeth. "Regression Analysis for Ordinal Outcomes in Matched Study Design: Applications to Alzheimer's Disease Studies." 2018. https://scholarworks.umass.edu/masters_theses_2/628.
Texte intégralRésumé :
Alzheimer's Disease (AD) affects nearly 5.4 million Americans as of 2016 and is the most common form of dementia. The disease is characterized by the presence of neurofibrillary tangles and amyloid plaques [1]. The amount of plaques are measured by Braak stage, post-mortem. It is known that AD is positively associated with hypercholesterolemia [16]. As statins are the most widely used cholesterol-lowering drug, there may be associations between statin use and AD. We hypothesize that those who use statins, specifically lipophilic statins, are more likely to have a low Braak stage in post-mortem analysis.
In order to address this hypothesis, we wished to fit a regression model for ordinal outcomes (e.g., high, moderate, or low Braak stage) using data collected from the National Alzheimer's Coordinating Center (NACC) autopsy cohort. As the outcomes were matched on the length of follow-up, a conditional likelihood-based method is often used to estimate the regression coefficients. However, it can be challenging to solve the conditional-likelihood based estimating equation numerically, especially when there are many matching strata. Given that the likelihood of a conditional logistic regression model is equivalent to the partial likelihood from a stratified Cox proportional hazard model, the existing R function for a Cox model, coxph( ), can be used for estimation of a conditional logistic regression model. We would like to investigate whether this strategy could be extended to a regression model for ordinal outcomes.
More specifically, our aims are to (1) demonstrate the equivalence between the exact partial likelihood of a stratified discrete time Cox proportional hazards model and the likelihood of a conditional logistic regression model, (2) prove equivalence, or lack there-of, between the exact partial likelihood of a stratified discrete time Cox proportional hazards model and the conditional likelihood of models appropriate for multiple ordinal outcomes: an adjacent categories model, a continuation-ratio model, and a cumulative logit model, and (3) clarify how to set up stratified discrete time Cox proportional hazards model for multiple ordinal outcomes with matching using the existing coxph( ) R function and interpret the regression coefficient estimates that result. We verified this theoretical proof through simulation studies. We simulated data from the three models of interest: an adjacent categories model, a continuation-ratio model, and a cumulative logit model. We fit a Cox model using the existing coxph( ) R function to the simulated data produced by each model. We then compared the coefficient estimates obtained. Lastly, we fit a Cox model to the NACC dataset. We used Braak stage as the outcome variables, having three ordinal categories. We included predictors for age at death, sex, genotype, education, comorbidities, number of days having taken lipophilic statins, number of days having taken hydrophilic statins, and time to death. We matched cases to controls on the length of follow up. We have discussed all findings and their implications in detail.
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Turan, Esra. "Mixture of Factor Analyzers with Information Criteria and the Genetic Algorithm." 2010. http://trace.tennessee.edu/utk_graddiss/853.
Texte intégralRésumé :
In this dissertation, we have developed and combined several statistical techniques in Bayesian factor analysis (BAYFA) and mixture of factor analyzers (MFA) to overcome the shortcoming of these existing methods. Information Criteria are brought into the context of the BAYFA model as a decision rule for choosing the number of factors m along with the Press and Shigemasu method, Gibbs Sampling and Iterated Conditional Modes deterministic optimization. Because of sensitivity of BAYFA on the prior information of the factor pattern structure, the prior factor pattern structure is learned directly from the given sample observations data adaptively using Sparse Root algorithm.
Clustering and dimensionality reduction have long been considered two of the fundamental problems in unsupervised learning or statistical pattern recognition. In this dissertation, we shall introduce a novel statistical learning technique by focusing our attention on MFA from the perspective of a method for model-based density estimation to cluster the high-dimensional data and at the same time carry out factor analysis to reduce the curse of dimensionality simultaneously in an expert data mining system. The typical EM algorithm can get trapped in one of the many local maxima therefore, it is slow to converge and can never converge to global optima, and highly dependent upon initial values. We extend the EM algorithm proposed by cite{Gahramani1997} for the MFA using intelligent initialization techniques, K-means and regularized Mahalabonis distance and introduce the new Genetic Expectation Algorithm (GEM) into MFA in order to overcome the shortcomings of typical EM algorithm. Another shortcoming of EM algorithm for MFA is assuming the variance of the error vector and the number of factors is the same for each mixture. We propose Two Stage GEM algorithm for MFA to relax this constraint and obtain different numbers of factors for each population. In this dissertation, our approach will integrate statistical modeling procedures based on the information criteria as a fitness function to determine the number of mixture clusters and at the same time to choose the number factors that can be extracted from the data.
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Rahmawati, Evi. "Information content and determinants of timeliness of financial reporting of manufacturing firms in Indonesia." Thesis, 2013. https://vuir.vu.edu.au/24830/.
Texte intégralRésumé :
One of the essential elements of adequate financial reporting is the provision of financial information that is relevant to its users in their decision-making. This financial information should be made available to users within a regulated short period after the end of the financial year. Agency theory suggests that shareholders require protection because management may not always act in the best interests of shareholders. Therefore, timely reporting is important in reducing information asymmetry between management and shareholders, and it may reduce leaks of financial information in an emerging market, such as in Indonesia‘s capital market.
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Rachtan, Piotr J. "Spatial and Temporal Correlations of Freeway Link Speeds: An Empirical Study." 2012. https://scholarworks.umass.edu/theses/940.
Texte intégralRésumé :
Congestion on roadways and high level of uncertainty of traffic conditions are major considerations for trip planning. The purpose of this research is to investigate the characteristics and patterns of spatial and temporal correlations and also to detect other variables that affect correlation in a freeway setting. 5-minute speed aggregates from the Performance Measurement System (PeMS) database are obtained for two directions of an urban freeway – I-10 between Santa Monica and Los Angeles, California. Observations are for all non-holiday weekdays between January 1st and June 30th, 2010. Other variables include traffic flow, ramp locations, number of lanes and the level of congestion at each detector station. A weighted least squares multilinear regression model is fitted to the data; the dependent variable is Fisher Z transform of correlation coefficient.
Estimated coefficients of the general regression model indicate that increasing spatial and temporal distances reduces correlations. The positive parameters of spatial and temporal distance interaction term show that the reduction rate diminishes with spatial or temporal distance. Higher congestion tends to retain higher expected value of correlation; corrections to the model due to variations in road geometry tend to be minor. The general model provides a framework for building a family of more responsive and better-fitting models for a 6.5 mile segment of the freeway during three times of day: morning, midday, and afternoon.
Each model is cross-validated on two locations: the opposite direction of the freeway, and a different location on the direction used for estimation. Cross-validation results show that models are able to retain 75% or more of their original predictive capability on independent samples. Incorporation of predictor variables that describe road geometry and traffic conditions into the model works beneficially in capturing a significant portion of variance of the response. The developed regression models are thus transferrable and are apt to predict correlation on other freeway locations.
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FALCONI, MARTA TECLA. "Remote sensing and electromagnetic modeling applied to weather and forward scatter radar." Doctoral thesis, 2018. http://hdl.handle.net/11573/1080160.
Texte intégralRésumé :
This dissertation deals with electromagnetic modelling and data analysis,
related to radar remote sensing and applied to forward scatter and meteorological
polarimetric systems. After an overview of radar fundamentals to
introduce the general terminology and concepts, results are presented at
the end of each chapter.
In this respect, a generalized electromagnetic model is first presented
in order to predict the response of forward scatter radars (FSRs) for airtarget
surveillance applications in both near-field and far-field regions.
The model is discussed for increasing levels of complexity: a simplified
near-field model, a near-field receiver model and a near-field receiver and
transmitter model. FSR results have been evaluated in terms of the effects
of different target electrical sizes and detection distances on the received
signal, as well as the impact of the trajectory of the moving objects and
compared with a customized implementation of a full-wave numerical
tool.
Secondly, a new data processing methodology, based on the statistical
analysis of ground-clutter echoes and aimed at investigating the monitoring
of the weather radar relative calibration, is presented. A preliminary
study for an improvement of the ground-clutter calibration technique is
formulated using as a permanent scatter analysis (PSA) and applied to
real radar scenarios. The weather radar relative calibration has been applied
to a dataset collected by a C-band weather radar in southern Italy
and an evaluation with statistical score indexes has drawn through the
comparison with a deterministic clutter map. The PSA technique has been
proposed using a big metallic roof with a periodic mesh grid structure and
having a hemispherical shape in the near-field of a polarimetric C-band
radar and evaluated also with an ad-hoc numerical implementation of a
full-wave solution.
Finally, a radar-based snowfall intensity retrieval is investigated at centimeter
and millimeter wavelengths (i.e., at X, Ka and W band) using a
high-quality database of collocated ground-based precipitation measurements
and radar multi-frequency observations. Coefficients for the multifrequency
radar snowfall intensity retrieval are empirically derived using
multivariate regression techniques and their interpretation is carried out
by particle scattering simulations with soft-ice spheroids.
For each topic, conclusions are proposed to highlight the goals of the
whole work and pave the way for future studies.