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Dissertations / Theses on the topic 'Nonparametrica'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

CORRADIN, RICCARDO. "Contributions to modelling via Bayesian nonparametric mixtures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241261.

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I modelli mistura in ambito Bayesiano nonparametrico sono modelli flessibili per stime di densità e clustering, ormai uno strumento di uso comune in ambito statistico applicato. Il primo modello introdotto in questo ambito è stato il processo di Dirichlet (DP) (Ferguson, 1973) combinato con un kernel Gaussiano(Lo, 1984). Recentemente è cresciuto l’interesse verso la definizione di modelli mistura basati su misure nonparametriche che generalizzano il DP. Tra le misure proposte, il processo di Pitman-Yor (PY) (Perman et al., 1992; Pitman, 1995) e, più in generale, la classe di Gibbs-type prior (

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2

Campbell, Trevor D. J. (Trevor David Jan). "Truncated Bayesian nonparametrics." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107047.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 167-175).<br>Many datasets can be thought of as expressing a collection of underlying traits with unknown cardinality. Moreover, these datasets are often persistently growing, and we expect the number of expressed traits to likewise increase over time. Priors from Bayesian nonparametrics are well-suited to this modeling challenge: they generate a countably infinite number of underlying traits, which allow

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3

Li, Jiexiang. "Nonparametric spatial estimation." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3223036.

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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2006.<br>"Title from dissertation home page (viewed June 28, 2007)." Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3167. Adviser: Lanh Tat Tran.

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Straub, Julian Ph D. Massachusetts Institute of Technology. "Nonparametric directional perception." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112029.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 239-257).<br>Artificial perception systems, like autonomous cars and augmented reality headsets, rely on dense 3D sensing technology such as RGB-D cameras and LiDAR. scanners. Due to the structural simplicity of man-made environments, understanding and leveraging not only the 3D data but also the local orientations of the constituent surfaces, has huge potential. From an indoor scene to lar

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5

Xu, Tianbing. "Nonparametric evolutionary clustering." Diss., Online access via UMI:, 2009.

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6

Rangel, Ruiz Ricardo. "Nonparametric and semi-nonparametric approaches to the demand for liquid assets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ64924.pdf.

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7

Yuan, Lin. "Bayesian nonparametric survival analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22253.pdf.

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8

Bush, Helen Meyers. "Nonparametric multivariate quality control." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/25571.

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9

Pedroso, Estevam de Souza Camila. "Switching nonparametric regression models." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45130.

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In this thesis, we propose a methodology to analyze data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z, also called a hidden or latent state. The states form a stochastic process and their possible values are j=1,...,J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. In a Bayesian switching nonparametric regression model the uncertainty a

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10

Gosling, John Paul. "Elicitation : a nonparametric view." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425613.

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11

Joseph, Joshua Mason. "Nonparametric Bayesian behavior modeling." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45263.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.<br>Includes bibliographical references (p. 91-94).<br>As autonomous robots are increasingly used in complex, dynamic environments, it is crucial that the dynamic elements are modeled accurately. However, it is often difficult to generate good models due to either a lack of domain understanding or the domain being intractably large. In many domains, even defining the size of the model can be a challenge. While methods exist to cluster data of dynamic agents into common motion patterns, or "behavio

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12

Lin, Lizhen. "Nonparametric Inference for Bioassay." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/222849.

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This thesis proposes some new model independent or nonparametric methods for estimating the dose-response curve and the effective dosage curve in the context of bioassay. The research problem is also of importance in environmental risk assessment and other areas of health sciences. It is shown in the thesis that our new nonparametric methods while bearing optimal asymptotic properties also exhibit strong finite sample performance. Although our specific emphasis is on bioassay and environmental risk assessment, the methodology developed in this dissertation applies broadly to general order res

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13

Minello, Giorgia <1983&gt. "Nonparametric Spectral Graph Model." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/5390.

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In many real world cases a feature-based description of objects is difficult and for this reason the use of the graph-based representation has become popular, thanks to the ability to effectively characterizing data. Learning models for detecting and classifying object categories is a challenging problem in machine vision, above all when objects are not described in a vectorial manner. Measuring their structural similarity, as well as characterizing a set of graphs via a representative, are only some of the several hurdles. A novel technique to classify objects abstracted in structured manner

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14

Scherreik, Matthew D. "Online Clustering with Bayesian Nonparametrics." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1610711743492959.

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15

Houtman, Martijn. "Nonparametric consumer and producer analysis." [Maastricht : Maastricht : Rijksuniversiteit Limburg] ; University Library, Maastricht University [Host], 1995. http://arno.unimaas.nl/show.cgi?fid=5770.

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16

Lee, Soyeon. "Spatial fixed design nonparametric regression." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3223074.

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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2006.<br>"Title from dissertation home page (viewed July 2, 2007)." Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3167. Adviser: Lanh Tran.

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17

Rensfelt, Agnes. "Nonparametric identification of viscoelastic materials." Licentiate thesis, Uppsala : Univ. : Dept. of Information Technology, Univ, 2006. http://www.it.uu.se/research/publications/lic/2006-008/2006-008.pdf.

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18

Kankey, Roland Doyle. "Nonparametric extrapolative forecasting : an evaluation." Connect to resource, 1988. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1265129995.

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19

Godefay, Dawit Zerom. "Nonparametric prediction: some selected topics." [Amsterdam : Amsterdam : Thela Thesis] ; Universiteit van Amsterdam [Host], 2002. http://dare.uva.nl/document/64443.

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20

Polsen, Orathai. "Nonparametric regression and mixture models." Thesis, University of Leeds, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578651.

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Nonparametric regression estimation has become popular in the last 50 years. A commonly used nonparametric method for estimating the regression curve is the kernel estimator, exemplified by the Nadaraya- Watson estimator. The first part of thesis concentrates on the important issue of how to make a good choice of smoothing parameter for the Nadaraya- Watson estimator. In this study three types of smoothing parameter selectors are investigated: cross-validation, plug-in and bootstrap. In addition, two situations are examined: the same smoothing parameter and different smoothing parameters are e

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21

Liu, Han. "Nonparametric Learning in High Dimensions." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/16.

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This thesis develops flexible and principled nonparametric learning algorithms to explore, understand, and predict high dimensional and complex datasets. Such data appear frequently in modern scientific domains and lead to numerous important applications. For example, exploring high dimensional functional magnetic resonance imaging data helps us to better understand brain functionalities; inferring large-scale gene regulatory network is crucial for new drug design and development; detecting anomalies in high dimensional transaction databases is vital for corporate and government security. Our

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22

Van, Gael Jurgen. "Bayesian nonparametric hidden Markov models." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610196.

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23

Baiocchi, Giovanni. "Economic applications of nonparametric methods." Thesis, University of York, 2006. http://etheses.whiterose.ac.uk/14117/.

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This thesis deals with the subject of nonparametric methods, focusing on application to economic issues. Chapter 2 introduces the basic nonparametric methods underlying the applications in the subsequent chapters. In Chapter 3 we propose some basic standards to improve the use and reporting of nonparametric methods in the statistics and economics literature for the purpose of accuracy and reproducibility. We make recommendations on four aspects of the application of nonparametric methods: computational practice, published reporting, numerical accuracy, and visualization. In Chapter 4 we invest

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24

Keys, Anthony C. "Nonparametric metamodeling for simulation optimization." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/38570.

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Optimization of simulation model performance requires finding the values of the model's controllable inputs that optimize a chosen model response. Responses are usually stochastic in nature, and the cost of simulation model runs is high. The literature suggests the use of metamodels to synthesize the response surface using sample data. In particular, nonparametric regression is proposed as a useful tool in the global optimization of a response surface. As the general simulation optimization problem is very difficult and requires expertise from a number of fields, there is a growing consensus i

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25

Dallaire, Patrick. "Bayesian nonparametric latent variable models." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/26848.

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L’un des problèmes importants en apprentissage automatique est de déterminer la complexité du modèle à apprendre. Une trop grande complexité mène au surapprentissage, ce qui correspond à trouver des structures qui n’existent pas réellement dans les données, tandis qu’une trop faible complexité mène au sous-apprentissage, c’est-à-dire que l’expressivité du modèle est insuffisante pour capturer l’ensemble des structures présentes dans les données. Pour certains modèles probabilistes, la complexité du modèle se traduit par l’introduction d’une ou plusieurs variables cachées dont le rôle est d’exp

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26

Gao, Wenyu. "Advanced Nonparametric Bayesian Functional Modeling." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99913.

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Functional analyses have gained more interest as we have easier access to massive data sets. However, such data sets often contain large heterogeneities, noise, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modele

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27

Millen, Brian A. "Nonparametric tests for umbrella alternatives /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488205318508038.

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28

Jiang, Yong Carleton University Dissertation Management Studies. "Bankruptcy prediction - a nonparametric approach." Ottawa, 1993.

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29

Nudurupati, Sai Vamshidhar Abebe Asheber. "Robust nonparametric discriminant analysis procedures." Auburn, Ala, 2009. http://hdl.handle.net/10415/1605.

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30

Dong, Lei. "Nonparametric tests for longitudinal data." Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/2295.

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31

Centorrino, Samuele. "Causality, endogeneity and nonparametric estimation." Thesis, Toulouse 1, 2013. http://www.theses.fr/2013TOU10020/document.

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Cette thèse porte sur les problèmes de causalité et d'endogénéité avec estimation non-paramétrique de la fonction d’intérêt. On explore ces problèmes dans deux modèles différents. Dans le cas de données en coupe transversale et iid, on considère l'estimation d'un modèle additif séparable, dans lequel la fonction de régression dépend d'une variable endogène. L'endogénéité est définie, dans ce cas, de manière très générale : elle peut être liée à une causalité inverse (la variable dépendante peut aussi intervenir dans la réalisation des régresseurs), ou à la simultanéité (les résidus contiennent

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32

Cortina, Borja Mario Jose Francisco. "Graph-theoretic multivariate nonparametric procedures." Thesis, University of Bath, 1992. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302730.

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33

Yanagi, Takahide. "Essays on Nonparametric Methods in Econometrics." Kyoto University, 2015. http://hdl.handle.net/2433/200427.

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34

Calle, M. Luz. "The analysis of interval-censored survival data. From a Nonparametric perspective to a nonparametric Bayesian approach." Doctoral thesis, Universitat Politècnica de Catalunya, 1997. http://hdl.handle.net/10803/6521.

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This work concerns some problems in the area of survival analysis that arise in real clinical or epidemiological studies. In particular, we approach the problem of estimating the survival function based on interval-censored data or doubly-censored data. We will start defining these concepts and presenting a brief review of different methodologies to deal with this kind of censoring patterns.<br/>Survival analysis is the term used to describe the analysis of data that correspond to the time from a well defined origin time until the occurrence of some particular event of interest. This event nee

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35

Sajama. "Nonparametric methods for learning from data." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3205362.

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Thesis (Ph. D.)--University of California, San Diego, 2006.<br>Title from first page of PDF file (viewed April 6, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 103-111).

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36

Guan, Yong Tao. "Nonparametric methods of assessing spatial isotropy." Texas A&M University, 2003. http://hdl.handle.net/1969.1/1158.

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A common requirement for spatial analysis is the modeling of the second-order structure. While the assumption of isotropy is often made for this structure, it is not always appropriate. A conventional practice to check for isotropy is to informally assess plots of direction-specific sample second-order properties, e.g., sample variogram or sample second-order intensity function. While a useful diagnostic, these graphical techniques are difficult to assess and open to interpretation. Formal alternatives to graphical diagnostics are valuable, but have been applied to a limited class of models.

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37

Habli, Nada. "Nonparametric Bayesian Modelling in Machine Learning." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34267.

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Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In this thesis, we examine the most popular priors used in Bayesian non-parametric inference. The Dirichlet process and its extensions are priors on an infinite-dimensional space. Originally introduced by Ferguson (1983), its conjugacy property allows a tractable posterior inference which has lately given rise to a significant developments in applications related to machine learning. Another yet widespread prior used in nonparametric Bayesian inference is the Beta process and its extensions. It has

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38

Aboalkhair, Ahmad M. "Nonparametric predictive inference for system reliability." Thesis, Durham University, 2012. http://etheses.dur.ac.uk/3918/.

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This thesis provides a new method for statistical inference on system reliability on the basis of limited information resulting from component testing. This method is called Nonparametric Predictive Inference (NPI). We present NPI for system reliability, in particular NPI for k-out-of-m systems, and for systems that consist of multiple ki-out-of-mi subsystems in series configuration. The algorithm for optimal redundancy allocation, with additional components added to subsystems one at a time is presented. We also illustrate redundancy allocation for the same system in case the costs of additio

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39

Dharmasena, Tibbotuwa Deniye Kankanamge Lasitha Sandamali, and Sandamali dharmasena@rmit edu au. "Sequential Procedures for Nonparametric Kernel Regression." RMIT University. Mathematical and Geospatial Sciences, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090119.134815.

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In a nonparametric setting, the functional form of the relationship between the response variable and the associated predictor variables is unspecified; however it is assumed to be a smooth function. The main aim of nonparametric regression is to highlight an important structure in data without any assumptions about the shape of an underlying regression function. In regression, the random and fixed design models should be distinguished. Among the variety of nonparametric regression estimators currently in use, kernel type estimators are most popular. Kernel type estimators provide a flexible c

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40

Huang, Fuping. "Nonparametric censored regression by smoothing splines." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ61977.pdf.

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41

Douglass, Julian James. "Nonparametric portfolio estimation and asset allocation." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/5414.

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This thesis comprises two essays that apply nonparametric methods to the estimation of portfolio allocations. In the first essay, I test the significance to investor welfare of (i) adding additional assets to the portfolio choice set and (ii) conditioning on predictor variables. I estimate unconditional and conditional optimal allocations of a constant relative risk aversion investor by maximizing a nonparametric approximation of the expected utility integral. Investors can improve their expected utility significantly over that of an equities and cash investor by adding portfolios based on th

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42

Samusenko, Pavel. "Nonparametric criteria for sparse contingency tables." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130218_142205-74244.

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In the dissertation, the problem of nonparametric testing for sparse contingency tables is addressed. Statistical inference problems caused by sparsity of contingency tables are widely discussed in the literature. Traditionally, the expected (under null the hypothesis) frequency is required to exceed 5 in almost all cells of the contingency table. If this condition is violated, the χ2 approximations of goodness of fit statistics may be inaccurate and the table is said to be sparse . Several techniques have been proposed to tackle the problem: exact tests, alternative approximations, parametric

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43

Denison, David George Taylor. "Simulation based Bayesian nonparametric regression methods." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266105.

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44

Maturi, Tahani. "Nonparametric predictive inference for multiple comparisons." Thesis, Durham University, 2010. http://etheses.dur.ac.uk/230/.

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This thesis presents Nonparametric Predictive Inference (NPI) for several multiple comparisons problems. We introduce NPI for comparison of multiple groups of data including right-censored observations. Different right-censoring schemes discussed are early termination of an experiment, progressive censoring and competing risks. Several selection events of interest are considered including selecting the best group, the subset of best groups, and the subset including the best group. The proposed methods use lower and upper probabilities for some events of interest formulated in terms of the next

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45

Elsaeiti, Mohamed. "Nonparametric predictive inference for acceptance decisions." Thesis, Durham University, 2011. http://etheses.dur.ac.uk/3442/.

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This thesis presents new solutions for two acceptance decisions problems. First, we present methods for basic acceptance sampling for attributes, based on the nonparametric predictive inferential approach for Bernoulli data, which is extended for this application. We consider acceptance sampling based on destructive tests and on non-destructive tests. Attention is mostly restricted to single stage sampling, but extension to two-stage sampling is also considered and discussed. Secondly, sequential acceptance decision problems are considered with the aim to select one or more candidates from a g

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Delatola, Eleni-Ioanna. "Bayesian nonparametric modelling of financial data." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589934.

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This thesis presents a class of discrete time univariate stochastic volatility models using Bayesian nonparametric techniques. In particular, the models that will be introduced are not only the basic stochastic volatility model, but also the heavy-tailed model using scale mixture of Normals and the leverage model. The aim will be focused on capturing flexibly the distribution of the logarithm of the squared return under the aforementioned models using infinite mixture of Normals. Parameter estimates for these models will be obtained using Markov chain Monte Carlo methods and the Kalman filter.

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47

Hall, Benjamin. "NONPARAMETRIC ESTIMATION OF DERIVATIVES WITH APPLICATIONS." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_diss/114.

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We review several nonparametric regression techniques and discuss their various strengths and weaknesses with an emphasis on derivative estimation and confidence band creation. We develop a generalized C(p) criterion for tuning parameter selection when interest lies in estimating one or more derivatives and the estimator is both linear in the observed responses and self-consistent. We propose a method for constructing simultaneous confidence bands for the mean response and one or more derivatives, where simultaneous now refers both to values of the covariate and to all derivatives under consid

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48

Benhaddou, Rida. "Nonparametric and Empirical Bayes Estimation Methods." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5765.

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In the present dissertation, we investigate two different nonparametric models; empirical Bayes model and functional deconvolution model. In the case of the nonparametric empirical Bayes estimation, we carried out a complete minimax study. In particular, we derive minimax lower bounds for the risk of the nonparametric empirical Bayes estimator for a general conditional distribution. This result has never been obtained previously. In order to attain optimal convergence rates, we use a wavelet series based empirical Bayes estimator constructed in Pensky and Alotaibi (2005). We prop

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49

Zychaluk, Kamila. "Application of noise in nonparametric curve." Thesis, University of Birmingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410854.

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PEREIRA, MANOEL FRANCISCO DE SOUZA. "OPTION PRICING VIA NONPARAMETRIC ESSCHER TRANSFORM." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19219@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>O apreçamento de opções é um dos temas mais importantes da economia financeira. Este estudo introduz uma versão não paramétrica da Transformada de Esscher para o apreçamento neutro ao risco de opções financeiras. Os tradicionais métodos paramétricos exigem a formulação de um modelo neutro ao risco explícito e são operacionalmente apenas para poucas funções densidade de probabilidade. Em nossa proposta, com simples suposições, evitamos a necessidade da formulação de um modelo neutro ao risco para os retornos. Primeiro, simulamos um

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