Progress In Data Based Bandwidth Selection For Kernel Density Estimation
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Progress In Data Based Bandwidth Selection For Kernel Density Estimation
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Author : M C. Jones
language : en
Publisher:
Release Date : 1992
Progress In Data Based Bandwidth Selection For Kernel Density Estimation written by M C. Jones and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Kernel functions categories.
On Optimal Data Based Bandwidth Selection In Kernel Density Estimation
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Author : Peter Hall
language : en
Publisher:
Release Date : 1989
On Optimal Data Based Bandwidth Selection In Kernel Density Estimation written by Peter Hall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Estimation theory 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.
Introduction To Aquifer Analysis
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Author : Michael Kasenow
language : en
Publisher: Water Resources Publication
Release Date : 1997
Introduction To Aquifer Analysis written by Michael Kasenow and has been published by Water Resources Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Aquifers categories.
Nonparametric Statistics And Mixture Models A Festschrift In Honor Of Thomas P Hettmansperger
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Author : David Hunter
language : en
Publisher: World Scientific
Release Date : 2011-01-03
Nonparametric Statistics And Mixture Models A Festschrift In Honor Of Thomas P Hettmansperger written by David Hunter and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-03 with Mathematics categories.
This festschrift includes papers authored by many collaborators, colleagues, and students of Professor Thomas P Hettmansperger, who worked in research in nonparametric statistics, rank statistics, robustness, and mixture models during a career that spanned nearly 40 years. It is a broad sample of peer-reviewed, cutting-edge research related to nonparametrics and mixture models.
Blind Signal Processing
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Author : Xizhi Shi
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-12-28
Blind Signal Processing written by Xizhi Shi 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 2011-12-28 with Technology & Engineering categories.
"Blind Signal Processing: Theory and Practice" not only introduces related fundamental mathematics, but also reflects the numerous advances in the field, such as probability density estimation-based processing algorithms, underdetermined models, complex value methods, uncertainty of order in the separation of convolutive mixtures in frequency domains, and feature extraction using Independent Component Analysis (ICA). At the end of the book, results from a study conducted at Shanghai Jiao Tong University in the areas of speech signal processing, underwater signals, image feature extraction, data compression, and the like are discussed. This book will be of particular interest to advanced undergraduate students, graduate students, university instructors and research scientists in related disciplines. Xizhi Shi is a Professor at Shanghai Jiao Tong University.
Handbook Of Econometrics
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Author : James J. Heckman
language : en
Publisher: Elsevier
Release Date : 2009-01-13
Handbook Of Econometrics written by James J. Heckman and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-13 with Business & Economics categories.
Multivariate Kernel Smoothing And Its Applications
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Author : José E. Chacón
language : en
Publisher: CRC Press
Release Date : 2018-05-08
Multivariate Kernel Smoothing And Its Applications written by José E. Chacón and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-08 with Mathematics categories.
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.
Kernel Smoothing
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Author : M.P. Wand
language : en
Publisher: CRC Press
Release Date : 1994-12-01
Kernel Smoothing written by M.P. Wand and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-12-01 with Mathematics categories.
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.
Xplore Learning Guide
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Author : W. Härdle
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Xplore Learning Guide written by W. Härdle 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 2012-12-06 with Computers categories.
It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This learning guide is intended for beginners in computer-aided statistical data analysis. The prerequisites for XploRe - the statistical computing environment - are an introductory course in statistics or mathematics. The reader of this book should be familiar with basic elements of matrix algebra and the use of HTML browsers. This guide is designed to help students to XploRe their data, to learn (via data interaction) about statistical methods and to disseminate their findings via the HTML outlet. The XploRe APSS (Auto Pilot Support System) is a powerful tool for finding the appropriate statistical technique (quantlet) for the data under analysis. Homogeneous quantlets are combined in XploRe into quantlibs. The XploRe language is intuitive and users with prior experience of other sta tistical programs will find it easy to reproduce the examples explained in this guide. The quantlets in this guide are available on the CD-ROM as well as on the Internet. The statistical operations that the student is guided into range from basic one-dimensional data analysis to more complicated tasks such as time series analysis, multivariate graphics construction, microeconometrics, panel data analysis, etc. The guide starts with a simple data analysis of pullover sales data, then in troduces graphics. The graphics are interactive and cover a wide range of dis plays of statistical data.