Relevant bibliographies by topics / Multivariate data analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multivariate data analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

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Journal articles on the topic "Multivariate data analysis"

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Fischer, Hagen S. "Multivariate analysis of phenological data." Phytocoenologia 30, no. 3-4 (November 24, 2000): 477–89. http://dx.doi.org/10.1127/phyto/30/2000/477.

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Qu, Xianggui. "Multivariate Data Analysis." Technometrics 49, no. 1 (February 2007): 103–4. http://dx.doi.org/10.1198/tech.2007.s455.

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Ziegel, Eric. "Multivariate Data Analysis." Technometrics 30, no. 1 (February 1988): 130–31. http://dx.doi.org/10.1080/00401706.1988.10488353.

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Schuenemeyer, John H. "Multivariate Data Analysis." Technometrics 31, no. 3 (August 1989): 393. http://dx.doi.org/10.1080/00401706.1989.10488578.

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Flury, Bernhard, Fionn Murtagh, and Andre Heck. "Multivariate Data Analysis." Mathematics of Computation 50, no. 181 (January 1988): 352. http://dx.doi.org/10.2307/2007941.

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Critchley, Frank, F. Murtagh, and A. Heck. "Multivariate Data Analysis." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 3 (1988): 563. http://dx.doi.org/10.2307/2983024.

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Wallbäcks, Lars. "Multivariate data analysis of multivariate populations." Chemometrics and Intelligent Laboratory Systems 86, no. 1 (March 2007): 10–16. http://dx.doi.org/10.1016/j.chemolab.2006.08.002.

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Naik, Dayanand N., Brian S. Everitt, and Graham Dunn. "Applied Multivariate Data Analysis." Technometrics 36, no. 2 (May 1994): 214. http://dx.doi.org/10.2307/1270233.

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Coates, Dave, Brian S. Everitt, and Graham Dunn. "Applied Multivariate Data Analysis." Journal of the Operational Research Society 45, no. 2 (February 1994): 237. http://dx.doi.org/10.2307/2584130.

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Williams, Calvin. "Applied Multivariate Data Analysis." American Statistician 56, no. 3 (August 2002): 248–49. http://dx.doi.org/10.1198/tas.2002.s4.

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Dissertations / Theses on the topic "Multivariate data analysis"

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Oliveira, Irene. "Correlated data in multivariate analysis." Thesis, University of Aberdeen, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401414.

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After presenting (PCA) Principal Component Analysis and its relationship with time series data sets, we describe most of the existing techniques in this field. Various techniques, e.g. Singular Spectrum Analysis, Hilbert EOF, Extended EOF or Multichannel Singular Spectrum Analysis (MSSA), Principal Oscillation Pattern Analysis (POP Analysis), can be used for such data. The way we use the matrix of data or the covariance or correlation matrix, makes each method different from the others. SSA may be considered as a PCA performed on a lagged versions of a single time series where we may decompose the original time series into some main components. Following SSA we have its multivariate version (MSSA) where we try to augment the initial matrix of data to get information on lagged versions of each variable (time series) and so past (or future) behaviour can be used to reanalyse the information between variables. In POP Analysis a linear system involving the vector field is analysed, xt+1=Axt+nt, in order to “know” xt at time t+1 given the information from time t. The matrix A is estimated by using not only the covariance matrix but also the matrix of covariances between the systems at the current time and at lag 1. In Hilbert EOF we try to get some (future) information from the internal correlation in each variable by using the Hilbert transform of each series in a augmented complex matrix with the data themselves in the real part and the Hilbert time series in the imaginary part Xt + XtH. In addition to all these ideas from the statistics and other literature we develop a new methodology as a modification of HEOF and POP Analysis, namely Hilbert Oscillation Patterns (HOP) Analysis or the related idea of Hilbert Canonical Correlation Analysis (HCCA), by using a system, xHt = Axt + nt. Theory and assumptions are presented and HOPS results will be related with the results extracted from a Canonical Correlation Analysis between the time series data matrix and its Hilbert transform. Some examples will be given to show the differences and similarities of the results of the HCCA technique with those from PCA, MSSA, HEOF and POPs. We also present PCA for time series as observations where a technique of linear algebra (PCA) becomes a problem in function analysis leading to Functional PCA (FPCA).  We also adapt PCA to allow for this and discuss the theoretical and practical behaviour of using PCA on the even part (EPCA) and odd part (OPCA) of the data, and its application in functional data. Comparisons will be made between PCA and this modification, for the reconstruction of data sets for which considerations of symmetry are especially relevant.
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Prelorendjos, Alexios. "Multivariate analysis of metabonomic data." Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=24286.

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Metabonomics is one of the main technologies used in biomedical sciences to improve understanding of how various biological processes of living organisms work. It is considered a more advanced technology than e.g. genomics and proteomics, as it can provide important evidence of molecular biomarkers for the diagnosis of diseases and the evaluation of beneficial adverse drug effects, by studying the metabolic profiles of living organisms. This is achievable by studying samples of various types such as tissues and biofluids. The findings of a metabonomics study for a specific disease, disorder or drug effect, could be applied to other diseases, disorders or drugs, making metabonomics an important tool for biomedical research. This thesis aims to review and study various multivariate statistical techniques which can be used in the exploratory analysis of metabonomics data. To motivate this research, a metabonomics data set containing the metabolic profiles of a group of patients with epilepsy was used. More specifically, the metabolic fingerprints (proton NMR spectra) of 125 patients with epilepsy, of blood serum type, have been obtained from the Western Infirmary, Glasgow, for the purposes of this project. These data were originally collected as baseline data in a study to investigate if the treatment with Anti-Epileptic Drugs (AEDs), of patients with pharmacoresistant epilepsy affects the seizure levels of the patients. The response to the drug treatment in terms of the reduction in seizure levels of these patients enabled two main categories of response to be identified, i.e. responders and the non-responders to AEDs. We explore the use of statistical methods used in metabonomics to analyse these data. Novel aspects of the thesis are the use of Self Organising Maps (SOM) and of Fuzzy Clustering Methods to pattern recognition in metabonomics data. Part I of the thesis defines metabonomics and the other main "omics" technologies, and gives a detailed description of the metabonomics data to be analysed, as well as a description of the two main analytical chemical techniques, Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR), that can be used to generate metabonomics data. Pre-processing and pre-treatment methods that are commonly used in NMR-generated metabonomics data to enhance the quality and accuracy of the data, are also discussed. In Part II, several unsupervised statistical techniques are reviewed and applied to the epilepsy data to investigate the capability of these techniques to discriminate the patients according to their type of response. The techniques reviewed include Principal Components Analysis (PCA), Multi-dimensional scaling (both Classical scaling and Sammon's non-linear mapping) and Clustering techniques. The latter include Hierarchical clustering (with emphasis on Agglomerative Nesting algorithms), Partitioning methods (Fuzzy and Hard clustering algorithms) and Competitive Learning algorithms (Self Organizing maps). The advantages and disadvantages of the different methods are examined, for this kind of data. Results of the exploratory multivariate analyses showed that no natural clusters of patients existed with regards to th eir response to AEDs, therefore none of these techniques was capable of discriminating these patients according to their clinical characteristics. To examine the capability of an unsupervised technique such as PCA, to identify groups in such data as the data based on metabolic fingerprints of patients with epilepsy, a simulation algorithm was developed to run a series of experiments, covered in Part III of the thesis. The aim of the simulation study is to investigate the extent of the difference in the clusters of the data, and under what conditions this difference is detectable by unsupervised techniques. Furthermore, the study examines whether the existence or lack of variation in the mean-shifted variables affects the discriminating ability of the unsupervised techniques (in this case PCA) or not. In each simulation experiment, a reference and a test data set were generated based on the original epilepsy data, and the discriminating capability of PCA was assessed. A test set was generated by mean-shifting a pre-selected number of variables in a reference set. Three methods of selecting the variables to meanshift (maximum and minimum standard deviations and maximum means), five subsets of variables of sizes 1, 3, 20, 120 and 244 (total number of variables in the data sets) and three sample sizes (100, 500 and 1000) were used. Average values in 100 runs of an experiment for two statistics, i.e. the misclassification rate and the average separation (Webb, 2002) were recorded. Results showed that the number of mean-shifted variables (in general) and the methods used to select the variables (in some cases) are important factors for the discriminating ability of PCA, whereas the sample size of the two data sets does not play any role in the experiments (although experiments in large sample sizes showed greater stability in the results for the two statistics in 100 runs of any experiment). The results have implications for the use of PCA with metabonomics data generally.
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Yang, Di. "Analysis guided visual exploration of multivariate data." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-050407-005925/.

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Lans, Ivo A. van der. "Nonlinear multivariate analysis for multiattribute preference data." [Leiden] : DSWO Press, Leiden University, 1992. http://catalog.hathitrust.org/api/volumes/oclc/28733326.html.

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Zhu, Liang. "Semiparametric analysis of multivariate longitudinal data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6044.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.
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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.

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Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial
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.
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Rehman, Naveed Ur. "Data-driven time-frequency analysis of multivariate data." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/9116.

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Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition and time-frequency analysis of real world nonstationary signals. Its main advantages over other time-frequency methods are its locality, data-driven nature, multiresolution-based decomposition, higher time-frequency resolution and its ability to capture oscillation of any type (nonharmonic signals). These properties have made EMD a viable tool for real world nonstationary data analysis. Recent advances in sensor and data acquisition technologies have brought to light new classes of signals containing typically several data channels. Currently, such signals are almost invariably processed channel-wise, which is suboptimal. It is, therefore, imperative to design multivariate extensions of the existing nonlinear and nonstationary analysis algorithms as they are expected to give more insight into the dynamics and the interdependence between multiple channels of such signals. To this end, this thesis presents multivariate extensions of the empirical mode de- composition algorithm and illustrates their advantages with regards to multivariate non- stationary data analysis. Some important properties of such extensions are also explored, including their ability to exhibit wavelet-like dyadic filter bank structures for white Gaussian noise (WGN), and their capacity to align similar oscillatory modes from multiple data channels. Owing to the generality of the proposed methods, an improved multi- variate EMD-based algorithm is introduced which solves some inherent problems in the original EMD algorithm. Finally, to demonstrate the potential of the proposed methods, simulations on the fusion of multiple real world signals (wind, images and inertial body motion data) support the analysis.
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Droop, Alastair Philip. "Correlation Analysis of Multivariate Biological Data." Thesis, University of York, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507622.

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Collins, Gary Stephen. "Multivariate analysis of flow cytometry data." Thesis, University of Exeter, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324749.

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Haydock, Richard. "Multivariate analysis of Raman spectroscopy data." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/30697/.

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This thesis is concerned with developing techniques for analysing Raman spectroscopic images. A Raman spectroscopic image differs from a standard image as in place of red, green and blue quantities for each pixel a Raman image contains a spectrum of light intensities at each pixel. These spectra are used to identify the chemical components from which the image subject, for example a tablet, is comprised. The study of these types of images is known as chemometrics, with the majority of chemometric methods based on multivariate statistical and image analysis techniques. The work in this thesis has two main foci. The first of these is on the spectral decomposition of a Raman image, the purpose of which is to identify the component chemicals and their concentrations. The standard method for this is to fit a bilinear model to the image where both parts of the model, representing components and concentrations, must be estimated. As the standard bilinear model is nonidentifiable in its solutions we investigate the range of possible solutions in the solution space with a random walk. We also derive an improved model for spectral decomposition, combining cluster analysis techniques and the standard bilinear model. For this purpose we apply the expectation maximisation algorithm on a Gaussian mixture model with bilinear means, to represent our spectra and concentrations. This reduces noise in the estimated chemical components by separating the Raman image subject from the background. The second focus of this thesis is on the analysis of our spectral decomposition results. For testing the chemical components for uniform mixing we derive test statistics for identifying patterns in the image based on Minkowski measures, grey level co-occurence matrices and neighbouring pixel correlations. However with a non-identifiable model any hypothesis tests performed on the solutions will be specific to only that solution. Therefore to obtain conclusions for a range of solutions we combined our test statistics with our random walk. We also investigate the analysis of a time series of Raman images as the subject dissolved. Using models comprised of Gaussian cumulative distribution functions we are able to estimate the changes in concentration levels of dissolving tablets between the scan times. The results of which allowed us to describe the dissolution process in terms of the quantities of component chemicals.
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Books on the topic "Multivariate data analysis"

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Hair, Joseph F. Multivariate data analysis with readings. 2nd ed. New York: Macmillan, 1987.

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E, Anderson Rolph, and Tatham Ronald L, eds. Multivariate data analysiswith readings. 2nd ed. New York: Macmillan, 1987.

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1949-, Dunn G., ed. Applied multivariate data analysis. New York: Oxford University Press, 1992.

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1949-, Dunn G., ed. Applied multivariate data analysis. 2nd ed. London: Arnold, 2001.

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F, Hair Joseph, ed. Multivariate data analysis. 6th ed. Upper Saddle River, N.J: Pearson Prentice Hall, 2005.

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Dominique, Guyot, Westad Frank, and Houmøller Lars P, eds. Multivariate data analysis: In practice : an introduction to multivariate data analysis and experimental design. 5th ed. Oslo: Camo Process AS, 2002.

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1949-, Dunn G., and Everitt Brian, eds. Applied multivariate data analysis. London: E. Arnold, 1991.

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Murtagh, Fionn, and André Heck. Multivariate Data Analysis. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5.

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Cooley, William W. Multivariate data analysis. Malabar, Fla: R.E. Krieger Pub. Co., 1985.

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F, Hair Joseph, ed. Multivariate data analysis. 5th ed. Englewood Cliffs, N.J: Prentice Hall, 1998.

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Book chapters on the topic "Multivariate data analysis"

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Introduction to Empirical Data Analysis." In Multivariate Analysis, 1–54. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_1.

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Everitt, Brian S., and Graham Dunn. "Multivariate Data and Multivariate Statistics." In Applied Multivariate Data Analysis, 1–8. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch1.

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Dugard, pat, John Todman, and Harry Staines. "Longitudinal data." In Approaching Multivariate Analysis, 359–76. 2nd ed. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003343097-15.

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Murtagh, Fionn, and André Heck. "Cluster Analysis." In Multivariate Data Analysis, 55–109. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_3.

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Murtagh, Fionn, and André Heck. "Discriminant Analysis." In Multivariate Data Analysis, 111–54. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_4.

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Bürgel, Oliver. "Multivariate Data Analysis." In The Internationalisation of British Start-up Companies in High-Technology Industries, 141–85. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57671-3_6.

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Haslwanter, Thomas. "Multivariate Data Analysis." In An Introduction to Statistics with Python, 221–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28316-6_12.

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Everitt, Brian Sidney. "Multivariate Data and Multivariate Analysis." In Springer Texts in Statistics, 1–15. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-124-5_1.

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Everitt, Brian, and Torsten Hothorn. "Multivariate Data and Multivariate Analysis." In An Introduction to Applied Multivariate Analysis with R, 1–24. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9650-3_1.

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Vehkalahti, Kimmo, and Brian S. Everitt. "Multivariate Data and Multivariate Analysis." In Multivariate Analysis for the Behavioral Sciences, 225–37. Second edition. | Boca Raton, Florida : CRC Press [2019] | Earlier edition published as: Multivariable modeling and multivariate analysis for the behavioral sciences / [by] Brian S. Everitt.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351202275-12.

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Conference papers on the topic "Multivariate data analysis"

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Kouvaras, George, and George Kokolakis. "Random Multivariate Multimodal Distributions." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0009.

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Nair, Shruti, Sungsoo Ha, and Wei Xu. "Data Analysis on Multivariate Image Set." In 2018 New York Scientific Data Summit (NYSDS). IEEE, 2018. http://dx.doi.org/10.1109/nysds.2018.8538941.

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Guo, Hanqi, He Xiao, Min Lu, and Xiaoru Yuan. "Scalable multivariate volume visualization and analysis." In 2011 IEEE Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2011. http://dx.doi.org/10.1109/ldav.2011.6092328.

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Stelzer, Joerg. "TMVA- the toolkit for multivariate data analysis." In XII Advanced Computing and Analysis Techniques in Physics Research. Trieste, Italy: Sissa Medialab, 2009. http://dx.doi.org/10.22323/1.070.0063.

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Lainscsek, Claudia, Manuel E. Hernandez, Howard Poizner, and Terrence J. Sejnowski. "Multivariate spectral analysis of electroencephalography data." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6696142.

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Therhaag, Jan, and TMVA Core Developer Team. "TMVA - Toolkit for multivariate data analysis." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2009: (ICCMSE 2009). AIP, 2012. http://dx.doi.org/10.1063/1.4771869.

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Di Yang, Elke A. Rundensteiner, and Matthew O. Ward. "Analysis Guided Visual Exploration of Multivariate Data." In 2007 IEEE Symposium on Visual Analytics Science and Technology. IEEE, 2007. http://dx.doi.org/10.1109/vast.2007.4389000.

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Liu, Shusen, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, and Valerio Pascucci. "Multivariate volume visualization through dynamic projections." In 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2014. http://dx.doi.org/10.1109/ldav.2014.7013202.

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Muir, E. R., I. J. Ndiour, N. A. Le Goasduff, R. A. Moffitt, Y. Liu, M. C. Sullards, A. H. Merrill, Y. Chen, and M. D. Wang. "Multivariate Analysis of Imaging Mass Spectrometry Data." In 7th IEEE International Conference on Bioinformatics and Bioengineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375603.

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Chou, Jui-Sheng, Hsin Wang, and Hsien-Cheng Tseng. "Project Data Warehouse Management with Multivariate Analysis." In 27th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 2010. http://dx.doi.org/10.22260/isarc2010/0046.

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Reports on the topic "Multivariate data analysis"

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Alam, M. Kathleen. Multivariate Analysis of Seismic Field Data. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/8993.

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Chen, Maximillian Gene, Kristin Marie Divis, James D. Morrow, and Laura A. McNamara. Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1472228.

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DeJong, Stephanie, Rosalie Multari, Kelsey Wilson, and Paiboon Tangyunyong. Evaluation of COTS Electronics by Power Spectrum Analysis and Multivariate Data Analysis. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1890397.

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Wong, George Y. Statistical Analysis of Multivariate Interval Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada409921.

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Grunsky, E. Spatial factor analysis: a technique to assess the spatial relationships of multivariate data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128074.

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Wong, George Y. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada418647.

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Wong, George. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390768.

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Mayer, B. P., D. A. Mew, A. DeHope, P. E. Spackman, and A. M. Williams. Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1366919.

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Mayer, B. P., C. A. Valdez, A. J. DeHope, P. E. Spackman, R. D. Sanner, H. P. Martinez, and A. M. Williams. Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1335778.

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Hassena, Amal ben, Hanen Sellami, Abdelkader Bougarech, Morsi Gdoura, Caroline Amiel, and Radhouane Gdoura. Differentiation of the Salmonella enterica Serovars Enteritidis and Kentucky Using Transmittance and Reflectance FTIR Spectroscopies and Multivariate Data Analysis. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, April 2021. http://dx.doi.org/10.7546/crabs.2021.04.14.

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