Nonparametric Probability Density Estimation
DOWNLOAD
Download Nonparametric Probability Density Estimation PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Nonparametric Probability Density Estimation book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Nonparametric Probability Density Estimation
DOWNLOAD
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 Density Estimation
DOWNLOAD
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.
A Non Parametric Probability Density Estimator And Some Applications
DOWNLOAD
Author : Ronald P. Fuchs
language : en
Publisher:
Release Date : 1984
A Non Parametric Probability Density Estimator And Some Applications written by Ronald P. Fuchs and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Density functionals categories.
In this thesis a new non-parametric probability density estimator is developed which has the following properties: (1) It yields a continuous, non-negative and piecewise linear estimate of a probability density function. (2) It converges to the true density function if the true density has no more than a finite number of discontinuities of a form where the value of the function at the discontinuity can be considered the average of the limiting values on either side of the discontinuity. (3) It requires no user supplied parameters. The estimator is shown to have significantly better error properties, for certain classes of distributions, than existing density estimators. The quality of the estimate is discussed, tabulated and graphically demonstrated. Applications, including parameterization, small sample analysis, and two sample tests are presented. These newly developed applications are shown to improve upon the generally accepted existing techniques. Guidelines for choosing a density estimation method along with an organized approach to method selection are discussed. Key words include: Statistical functions, Statistical tests, Nonparametric statistics, Probability density functions, Statistics.
Density Estimation For Statistics And Data Analysis
DOWNLOAD
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.
Nonparametric Probability Density Estimation
DOWNLOAD
Author : R. Tapia
language : en
Publisher:
Release Date : 1991
Nonparametric Probability Density Estimation written by R. Tapia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Distribucion (Teoria de la probabilidad) categories.
Nonparametric Kernel Density Estimation And Its Computational Aspects
DOWNLOAD
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.
Nonparametric Probability Density Estimation
DOWNLOAD
Author : Edward J. Wegman
language : en
Publisher:
Release Date : 1969
Nonparametric Probability Density Estimation written by Edward J. Wegman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1969 with categories.
Nonparametric Functional Estimation
DOWNLOAD
Author : B. L. S. Prakasa Rao
language : en
Publisher: Academic Press
Release Date : 2014-07-10
Nonparametric Functional Estimation written by B. L. S. Prakasa Rao and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-10 with Mathematics categories.
Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both for specialists in the area as well as for students of statistics taking courses at the postgraduate level. The main emphasis throughout the book is on the discussion of several methods of estimation and on the study of their large sample properties. Chapters are devoted to topics on estimation of density and related functions, the application of density estimation to classification problems, and the different facets of estimation of distribution functions. Statisticians and students of statistics and engineering will find the text very useful.
Nonparametric Probability Density Estimation For Data Analysis In Several Dimensions
DOWNLOAD
Author : David W. Scott
language : en
Publisher:
Release Date : 1983
Nonparametric Probability Density Estimation For Data Analysis In Several Dimensions written by David W. Scott and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with categories.
The purpose of this paper is to illustrate how nonparametric probability density estimates, in particular the corresponding contour curves, are a useful adjunct to scatter diagrams when performing a preliminary examination of a set of random data in several dimensions. For a preliminary approach we generally want to perform fairly simple tasks with free-form techniques to uncover structures and features of interest in the data. Such procedures are often graphical and unlike summary statistics seldom lead to much compression of the data. Tukey presents a wealth of such procedures. One which well illustrates the power and flexibility of these preliminary procedures is the running median smoothing algorithm for time series data (with resmoothing of the rough and the like). Other graphical techniques for multivariate data are presented in Tukey and Tukey. (Author).
Multivariate Density Estimation
DOWNLOAD
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.