Aspects Of Nonparametric Density Estimation

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Nonparametric Kernel Density Estimation and Its Computational Aspects

Author : Artur Gramacki
Publisher : Springer
ISBN 13 : 3319716883
Total Pages : 197 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Nonparametric Kernel Density Estimation and Its Computational Aspects by : Artur Gramacki

Download or read book Nonparametric Kernel Density Estimation and Its Computational Aspects written by Artur Gramacki and published by Springer. This book was released on 2017-12-21 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Aspects of Nonparametric Density Estimation

Author : A. J. van Es
Publisher :
ISBN 13 :
Total Pages : 158 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Aspects of Nonparametric Density Estimation by : A. J. van Es

Download or read book Aspects of Nonparametric Density Estimation written by A. J. van Es and published by . This book was released on 1991 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Aspects of Nonparametric Density Estimation

Author : Albertus Jacob van Es
Publisher :
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.:/5 (256 download)

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Book Synopsis Aspects of Nonparametric Density Estimation by : Albertus Jacob van Es

Download or read book Aspects of Nonparametric Density Estimation written by Albertus Jacob van Es and published by . This book was released on 1988 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Aspects of Nonparametric Density Estimation

Author : Albertus Jacob van Es
Publisher :
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.:/5 (648 download)

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Book Synopsis Aspects of Nonparametric Density Estimation by : Albertus Jacob van Es

Nonparametric Density Estimation

Author : Luc Devroye
Publisher : New York ; Toronto : Wiley
ISBN 13 :
Total Pages : 376 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Nonparametric Density Estimation by : Luc Devroye

Download or read book Nonparametric Density Estimation written by Luc Devroye and published by New York ; Toronto : Wiley. This book was released on 1985-01-18 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Richard A. Tapia
Publisher :
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4.3/5 (97 download)

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Book Synopsis Nonparametric Probability Density Estimation by : Richard A. Tapia

Download or read book Nonparametric Probability Density Estimation written by Richard A. Tapia and published by . This book was released on 1978 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Nonparametric Econometrics

Author : Qi Li
Publisher : Princeton University Press
ISBN 13 : 1400841062
Total Pages : 769 pages
Book Rating : 4.4/5 (8 download)

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Book Synopsis Nonparametric Econometrics by : Qi Li

Download or read book Nonparametric Econometrics written by Qi Li and published by Princeton University Press. This book was released on 2011-10-09 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.

Multivariate Kernel Smoothing and Its Applications

Author : José E. Chacón
Publisher : CRC Press
ISBN 13 : 0429939132
Total Pages : 327 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis Multivariate Kernel Smoothing and Its Applications by : José E. Chacón

Download or read book Multivariate Kernel Smoothing and Its Applications written by José E. Chacón and published by CRC Press. This book was released on 2018-05-08 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solutions to complex data challenges. Multivariate Kernel Smoothing and Its Applications offers a comprehensive overview of both aspects. It begins with a thorough exposition of the approaches to achieve the two basic goals of estimating probability density functions and their derivatives. The focus then turns to the applications of these approaches to more complex data analysis goals, many with a geometric/topological flavour, such as level set estimation, clustering (unsupervised learning), principal curves, and feature significance. Other topics, while not direct applications of density (derivative) estimation but sharing many commonalities with the previous settings, include classification (supervised learning), nearest neighbour estimation, and deconvolution for data observed with error. For a data scientist, each chapter contains illustrative Open data examples that are analysed by the most appropriate kernel smoothing method. The emphasis is always placed on an intuitive understanding of the data provided by the accompanying statistical visualisations. For a reader wishing to investigate further the details of their underlying statistical reasoning, a graduated exposition to a unified theoretical framework is provided. The algorithms for efficient software implementation are also discussed. José E. Chacón is an associate professor at the Department of Mathematics of the Universidad de Extremadura in Spain. Tarn Duong is a Senior Data Scientist for a start-up which provides short distance carpooling services in France. Both authors have made important contributions to kernel smoothing research over the last couple of decades.

Density Estimation for Statistics and Data Analysis

Author : Bernard. W. Silverman
Publisher : Routledge
ISBN 13 : 1351456172
Total Pages : 176 pages
Book Rating : 4.3/5 (514 download)

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Book Synopsis Density Estimation for Statistics and Data Analysis by : Bernard. W. Silverman

Download or read book Density Estimation for Statistics and Data Analysis written by Bernard. W. Silverman and published by Routledge. This book was released on 2018-02-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : David W. Scott
Publisher : John Wiley & Sons
ISBN 13 : 1118575482
Total Pages : 381 pages
Book Rating : 4.1/5 (185 download)

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Book Synopsis Multivariate Density Estimation by : David W. Scott

Download or read book Multivariate Density Estimation written by David W. Scott and published by John Wiley & Sons. This book was released on 2015-03-12 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Nonparametric and Semiparametric Models

Author : Wolfgang Karl Härdle
Publisher : Springer Science & Business Media
ISBN 13 : 364217146X
Total Pages : 317 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Nonparametric and Semiparametric Models by : Wolfgang Karl Härdle

Download or read book Nonparametric and Semiparametric Models written by Wolfgang Karl Härdle and published by Springer Science & Business Media. This book was released on 2012-08-27 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.

Combinatorial Methods in Density Estimation

Author : Luc Devroye
Publisher : Springer Science & Business Media
ISBN 13 : 1461301254
Total Pages : 219 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis Combinatorial Methods in Density Estimation by : Luc Devroye

Download or read book Combinatorial Methods in Density Estimation written by Luc Devroye and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 book is the first to explore 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.

Combinatorial Methods in Density Estimation

Author : Luc Devroye
Publisher : Springer Science & Business Media
ISBN 13 : 9780387951171
Total Pages : 228 pages
Book Rating : 4.9/5 (511 download)

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Book Synopsis Combinatorial Methods in Density Estimation by : Luc Devroye

Download or read book Combinatorial Methods in Density Estimation written by Luc Devroye and published by Springer Science & Business Media. This book was released on 2001-01-12 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Smoothing of Multivariate Data

Author : Jussi Sakari Klemelä
Publisher : John Wiley & Sons
ISBN 13 : 0470425660
Total Pages : 641 pages
Book Rating : 4.4/5 (74 download)

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Book Synopsis Smoothing of Multivariate Data by : Jussi Sakari Klemelä

Download or read book Smoothing of Multivariate Data written by Jussi Sakari Klemelä and published by John Wiley & Sons. This book was released on 2009-09-04 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: An applied treatment of the key methods and state-of-the-art tools for visualizing and understanding statistical data Smoothing of Multivariate Data provides an illustrative and hands-on approach to the multivariate aspects of density estimation, emphasizing the use of visualization tools. Rather than outlining the theoretical concepts of classification and regression, this book focuses on the procedures for estimating a multivariate distribution via smoothing. The author first provides an introduction to various visualization tools that can be used to construct representations of multivariate functions, sets, data, and scales of multivariate density estimates. Next, readers are presented with an extensive review of the basic mathematical tools that are needed to asymptotically analyze the behavior of multivariate density estimators, with coverage of density classes, lower bounds, empirical processes, and manipulation of density estimates. The book concludes with an extensive toolbox of multivariate density estimators, including anisotropic kernel estimators, minimization estimators, multivariate adaptive histograms, and wavelet estimators. A completely interactive experience is encouraged, as all examples and figurescan be easily replicated using the R software package, and every chapter concludes with numerous exercises that allow readers to test their understanding of the presented techniques. The R software is freely available on the book's related Web site along with "Code" sections for each chapter that provide short instructions for working in the R environment. Combining mathematical analysis with practical implementations, Smoothing of Multivariate Data is an excellent book for courses in multivariate analysis, data analysis, and nonparametric statistics at the upper-undergraduate and graduatelevels. It also serves as a valuable reference for practitioners and researchers in the fields of statistics, computer science, economics, and engineering.

Kernel Smoothing

Author : M.P. Wand
Publisher : CRC Press
ISBN 13 : 1482216124
Total Pages : 227 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Kernel Smoothing by : M.P. Wand

Download or read book Kernel Smoothing written by M.P. Wand and published by CRC Press. This book was released on 1994-12-01 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function. This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilita

All of Nonparametric Statistics

Author : Larry Wasserman
Publisher : Springer Science & Business Media
ISBN 13 : 0387306234
Total Pages : 272 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis All of Nonparametric Statistics by : Larry Wasserman

Download or read book All of Nonparametric Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2006-09-10 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.

Nonparametric Function Estimation, Modeling, and Simulation

Author : James R. Thompson
Publisher : SIAM
ISBN 13 : 9781611971712
Total Pages : 320 pages
Book Rating : 4.9/5 (717 download)

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Book Synopsis Nonparametric Function Estimation, Modeling, and Simulation by : James R. Thompson

Download or read book Nonparametric Function Estimation, Modeling, and Simulation written by James R. Thompson and published by SIAM. This book was released on 1990-01-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.