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Author : Albertus Jacob Es
Publisher :
ISBN 13 :
Total Pages : 13 pages
Book Rating : 4.:/5 (96 download)
Download or read book On the integrated squared error of a kernel density estimator in non-smooth cases written by Albertus Jacob Es and published by . This book was released on 1994 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Bert van Es
Publisher :
ISBN 13 :
Total Pages : 13 pages
Book Rating : 4.:/5 (467 download)
Download or read book On the Integrated Squared Error of a Kernel Density Estimator in Non-smooth Cases written by Bert van Es and published by . This book was released on 1994 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Let X1 ..., X[subscript n] be a random sample from a distribution on the real line with an unknown density f. We discuss the performance of the classical kernel density estimator of the density f. The properties of kernel estimators in cases where the density f to be estimated is sufficiently smooth are well known. Instead we focus on estimation problems where f is non-smooth, i.e. f is allowed to have a finite number of jumps or kinks. Thus the robustness properties of the kernel estimator against unfulfilled smoothness assumptions are illustrated. After a review of properties of the mean integrated squared error we present a central limit theorem for the integrated squared error. This theorem extends results of Bickel, Rosenblatt and Hall. Finally, the distance between the bandwidth minimizing the integrated squared error and the bandwidth which minimizes the mean integrated squared error is discussed."
Author : M.P. Wand
Publisher : CRC Press
ISBN 13 : 9780412552700
Total Pages : 230 pages
Book Rating : 4.5/5 (527 download)
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 230 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 facilitated by the authors' focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail. Kernal Smoothing is self-contained and assumes only a basic knowledge of statistics, calculus, and matrix algebra. It is an invaluable introduction to the main ideas of kernel estimation for students and researchers from other discipline and provides a comprehensive reference for those familiar with the topic.
Author : A. J. van Es
Publisher :
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4.:/5 (594 download)
Download or read book Kernel Estimators of Integrated Squared Density Derivatives in Non-smooth Cases written by A. J. van Es and published by . This book was released on 1993 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Dan Froelich
Publisher :
ISBN 13 :
Total Pages : 150 pages
Book Rating : 4.:/5 (489 download)
Download or read book Mean Integrated Squared Error in the Kernel Density Function Estimation written by Dan Froelich and published by . This book was released on 2009 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Qi Li
Publisher : Princeton University Press
ISBN 13 : 1400841062
Total Pages : 769 pages
Book Rating : 4.4/5 (8 download)
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.
Author : Nikolai G. Ushakov
Publisher : Walter de Gruyter
ISBN 13 : 3110935988
Total Pages : 369 pages
Book Rating : 4.1/5 (19 download)
Download or read book Selected Topics in Characteristic Functions written by Nikolai G. Ushakov and published by Walter de Gruyter. This book was released on 2011-11-02 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
Author : C. Van Eeden
Publisher :
ISBN 13 :
Total Pages : 11 pages
Book Rating : 4.:/5 (256 download)
Download or read book Mean Integrated Squared Error of Kernel Estimators when the Density and Its Derivative are Not Necessarily Continuous written by C. Van Eeden and published by . This book was released on 1984 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :
Publisher :
ISBN 13 :
Total Pages : 756 pages
Book Rating : 4.:/5 (7 download)
Download or read book Statistical Theory and Method Abstracts written by and published by . This book was released on 2001 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Sucharita Ghosh
Publisher : John Wiley & Sons
ISBN 13 : 111845605X
Total Pages : 272 pages
Book Rating : 4.1/5 (184 download)
Download or read book Kernel Smoothing written by Sucharita Ghosh and published by John Wiley & Sons. This book was released on 2018-01-09 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data e.g. with short-memory and long-memory correlations, as well as non-Gaussian data that are transformations of latent Gaussian processes. These types of data occur in many fields of research, e.g. the natural and the environmental sciences, and others. Nonparametric density estimation, nonparametric and semiparametric regression, trend and surface estimation in particular for time series and spatial data and other topics such as rapid change points, robustness etc. are introduced alongside a study of their theoretical properties and optimality issues, such as consistency and bandwidth selection. Addressing a variety of topics, Kernel Smoothing: Principles, Methods and Applications offers a user-friendly presentation of the mathematical content so that the reader can directly implement the formulas using any appropriate software. The overall aim of the book is to describe the methods and their theoretical backgrounds, while maintaining an analytically simple approach and including motivating examples—making it extremely useful in many sciences such as geophysics, climate research, forestry, ecology, and other natural and life sciences, as well as in finance, sociology, and engineering. A simple and analytical description of kernel smoothing methods in various contexts Presents the basics as well as new developments Includes simulated and real data examples Kernel Smoothing: Principles, Methods and Applications is a textbook for senior undergraduate and graduate students in statistics, as well as a reference book for applied statisticians and advanced researchers.
Author : Leonard A. Stefanski
Publisher :
ISBN 13 :
Total Pages : 30 pages
Book Rating : 4.:/5 (13 download)
Download or read book Deconvoluting Kernel Density Estimators written by Leonard A. Stefanski and published by . This book was released on 1989 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers estimation of a continuous bounded probability density when observations from the density are contaminated by additive measurement errors having a known distribution. Properties of the estimator obtained by deconvolving a kernel estimator of the observed data are investigated. When the kernel used is sufficiently smooth the deconvolved estimator is shown to be pointwise consistent and bounds on its integrated mean squared error are derived. Very weak assumptions are made on the measurement-error density thereby permitting a comparison of the effects of different types of measurement error on the deconvolved estimator.
Author : Bert van Es
Publisher :
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4.:/5 (249 download)
Download or read book On the Expansion of the Mean Integrated Squared Error of a Kernel Density Estimator written by Bert van Es and published by . This book was released on 2000 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Bernard. W. Silverman
Publisher : Routledge
ISBN 13 : 1351456164
Total Pages : 190 pages
Book Rating : 4.3/5 (514 download)
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 190 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.
Author : Petr Mandl
Publisher : Springer Science & Business Media
ISBN 13 : 3642579841
Total Pages : 463 pages
Book Rating : 4.6/5 (425 download)
Download or read book Asymptotic Statistics written by Petr Mandl and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: In particular up-to-date-information is presented in detection of systematic changes, in series of observation, in robust regression analysis, in numerical empirical processes and in related areas of actuarial sciences.
Author : A. J. van Es
Publisher :
ISBN 13 :
Total Pages : 158 pages
Book Rating : 4.3/5 (91 download)
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:
Author : N. H. Bingham
Publisher : Cambridge University Press
ISBN 13 : 1139487922
Total Pages : 547 pages
Book Rating : 4.1/5 (394 download)
Download or read book Probability and Mathematical Genetics written by N. H. Bingham and published by Cambridge University Press. This book was released on 2010-07-15 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: No leading university department of mathematics or statistics, or library, can afford to be without this unique text. Leading authorities give a unique insight into a wide range of currently topical problems, from the mathematics of road networks to the genomics of cancer.
Author : Bernard. W. Silverman
Publisher : Routledge
ISBN 13 : 1351456172
Total Pages : 176 pages
Book Rating : 4.3/5 (514 download)
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.
