Nonparametric Density Estimation
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Nonparametric Density Estimation
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Author : Luc Devroye
language : en
Publisher: New York ; Toronto : Wiley
Release Date : 1985-01-18
Nonparametric Density Estimation written by Luc Devroye and has been published by New York ; Toronto : Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-01-18 with Mathematics categories.
This book gives a rigorous, systematic treatment of density estimates, their construction, use and analysis with full proofs. It develops L1 theory, rather than the classical L2, showing how L1 exposes fundamental properties of density estimates masked by L2.
Nonparametric Probability Density Estimation
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Author : Richard A. Tapia
language : en
Publisher:
Release Date : 1978
Nonparametric Probability Density Estimation written by Richard A. Tapia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978 with Mathematics categories.
Nonparametric Kernel Density Estimation And Its Computational Aspects
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Author : Artur Gramacki
language : en
Publisher: Springer
Release Date : 2017-12-21
Nonparametric Kernel Density Estimation And Its Computational Aspects written by Artur Gramacki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-21 with Technology & Engineering categories.
This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.
Density Estimation For Statistics And Data Analysis
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Author : Bernard. W. Silverman
language : en
Publisher: Routledge
Release Date : 2018-02-19
Density Estimation For Statistics And Data Analysis written by Bernard. W. Silverman and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-19 with Mathematics categories.
Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique's practical value. Furthermore, the subject has been rather inaccessible to the general statistician. The account presented in this book places emphasis on topics of methodological importance, in the hope that this will facilitate broader practical application of density estimation and also encourage research into relevant theoretical work. The book also provides an introduction to the subject for those with general interests in statistics. The important role of density estimation as a graphical technique is reflected by the inclusion of more than 50 graphs and figures throughout the text. Several contexts in which density estimation can be used are discussed, including the exploration and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates and other quantities that depend on the density. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects. Attention is also given to adaptive methods, which smooth to a greater degree in the tails of the distribution, and to methods based on the idea of penalized likelihood.
Multivariate Density Estimation
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Author : David W. Scott
language : en
Publisher: John Wiley & Sons
Release Date : 2015-03-12
Multivariate Density Estimation written by David W. Scott and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-12 with Mathematics categories.
Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features: Over 150 updated figures to clarify theoretical results and to show analyses of real data sets An updated presentation of graphic visualization using computer software such as R A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering More than 130 problems to help readers reinforce the main concepts and ideas presented Boxed theorems and results allowing easy identification of crucial ideas Figures in color in the digital versions of the book A website with related data sets Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.
Combinatorial Methods In Density Estimation
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Author : Luc Devroye
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-01-12
Combinatorial Methods In Density Estimation written by Luc Devroye and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-01-12 with Mathematics categories.
Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric. It is the first book on this topic. The text is intended for first-year graduate students in statistics and learning theory, and offers a host of opportunities for further research and thesis topics. Each chapter corresponds roughly to one lecture, and is supplemented with many classroom exercises. A one year course in probability theory at the level of Feller's Volume 1 should be more than adequate preparation. Gabor Lugosi is Professor at Universitat Pompeu Fabra in Barcelona, and Luc Debroye is Professor at McGill University in Montreal. In 1996, the authors, together with Lászlo Györfi, published the successful text, A Probabilistic Theory of Pattern Recognition with Springer-Verlag. Both authors have made many contributions in the area of nonparametric estimation.
Nonparametric Function Estimation Modeling And Simulation
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Author : James R. Thompson
language : en
Publisher: SIAM
Release Date : 1990-01-01
Nonparametric Function Estimation Modeling And Simulation written by James R. Thompson and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-01-01 with Mathematics categories.
Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions.
Nonparametric Density Estimation Via Regularization
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Author : Mu Lin
language : en
Publisher:
Release Date : 2009
Nonparametric Density Estimation Via Regularization written by Mu Lin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Density functionals categories.
Nonparametric Model Selection
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Author : Maurizio Tiso
language : en
Publisher:
Release Date : 1999
Nonparametric Model Selection written by Maurizio Tiso and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
Nonparametric Density Estimation Using Correlated Data With Application To Screening Problems
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Author : Junjun Qin
language : en
Publisher:
Release Date : 2013
Nonparametric Density Estimation Using Correlated Data With Application To Screening Problems written by Junjun Qin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
To detect a rare phenomenon with a higher probability by means of some other variable(s) which is (are) highly related but easier in technology or cheaper in expenditure is termed as "screening". It has come to be realized in recent years that current methodologies are of limited practical value. They are either built on a parametric framework which in fact we have little knowledge about or little involved with "multivariate screening" and "multi-stage screening", which are more frequent and more important in practice. Therefore, the alternative method discussed here suggests that we consider a nonparametric density estimation using correlated data. This one derives from and greatly formulates a rough idea that screening in nature is a kind of problems about how to solve probabilities and thus we intuitively look for estimating the probability density function (pdf). Since we have little information about data in a regular setting, nonparametric estimation seems a best candidate. In this study, the approach is presented by firstly making nonparametric density estimation in the case where random variables X and Y are both continuous and in the case where X is continuous but Y is discrete respectively. The behaviors under the large sample setting are explored and the performance of the proposed method is evaluated through simulations. The proposed method is then applied to a subsequent screening problem. The comparison with some current method is also presented.