Bootstrap Methods
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Bootstrap Methods
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Author : Michael R. Chernick
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-23
Bootstrap Methods written by Michael R. Chernick 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 2011-09-23 with Mathematics categories.
A practical and accessible introduction to the bootstrap method——newly revised and updated Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics. Updated to reflect current techniques and the most up-to-date work on the topic, the Second Edition features: The addition of a second, extended bibliography devoted solely to publications from 1999–2007, which is a valuable collection of references on the latest research in the field A discussion of the new areas of applicability for bootstrap methods, including use in the pharmaceutical industry for estimating individual and population bioequivalence in clinical trials A revised chapter on when and why bootstrap fails and remedies for overcoming these drawbacks Added coverage on regression, censored data applications, P-value adjustment, ratio estimators, and missing data New examples and illustrations as well as extensive historical notes at the end of each chapter With a strong focus on application, detailed explanations of methodology, and complete coverage of modern developments in the field, Bootstrap Methods, Second Edition is an indispensable reference for applied statisticians, engineers, scientists, clinicians, and other practitioners who regularly use statistical methods in research. It is also suitable as a supplementary text for courses in statistics and resampling methods at the upper-undergraduate and graduate levels.
Bootstrap Methods And Their Application
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Author : A. C. Davison
language : en
Publisher: Cambridge University Press
Release Date : 1997-10-28
Bootstrap Methods And Their Application written by A. C. Davison and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-10-28 with Computers categories.
Disk contains the library functions and documentation for use with Splus for Windows.
Bootstrap Methods
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Author : Michael R. Chernick
language : en
Publisher: Wiley-Interscience
Release Date : 1999-09-16
Bootstrap Methods written by Michael R. Chernick and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-09-16 with Mathematics categories.
A comprehensive, practical treatment for professionals In less than two decades, the bootstrap has grown from an obscure object of theoretical study to a widely used resampling method with broad applications in numerous real-world situations. Bootstrap Methods: A Practitioner's Guide provides an introduction to the bootstrap for readers who have professional interest in these methods but do not have a background in advanced mathematics. It offers reliable, authoritative coverage of the bootstrap's considerable advantages as well as its drawbacks. This book updates classic texts in the field by presenting results on improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems. To alert readers to the limitations of the method, it exhibits counterexamples to the consistency of bootstrap methods. This book also makes connections between more traditional resampling methods and bootstrap. Outstanding special features of Bootstrap Methods include: * The most extensive and detailed bootstrap bibliography available, including more than 1,600 references * Discussions enlivened with stimulating topics such as data mining * Historical notes at the end of each chapter * Examples and explanations of when and why bootstrap is not effective Bootstrap Methods is a serious, useful, and unparalleled practical guide for professionals in engineering, the sciences, clinical medicine, and applied statistics.
Bootstrap Methods
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Author : Gerhard Dikta
language : en
Publisher: Springer Nature
Release Date : 2021-08-10
Bootstrap Methods written by Gerhard Dikta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-10 with Mathematics categories.
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
Asymptotic Theory For Bootstrap Methods In Statistics
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Author : Rudolf J. Beran
language : en
Publisher: Publications CRM
Release Date : 1991
Asymptotic Theory For Bootstrap Methods In Statistics written by Rudolf J. Beran and has been published by Publications CRM this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Asymptotic efficiencies (Statistics) categories.
An Introduction To Bootstrap Methods With Applications To R
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Author : Michael R. Chernick
language : en
Publisher: John Wiley & Sons
Release Date : 2014-08-21
An Introduction To Bootstrap Methods With Applications To R written by Michael R. Chernick 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 2014-08-21 with Mathematics categories.
A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments. The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study. Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.
Bootstrap Methods For Density Estimates And K Means Clustering
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Author : Myoungshic Jhun
language : en
Publisher:
Release Date : 1985
Bootstrap Methods For Density Estimates And K Means Clustering written by Myoungshic Jhun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with categories.
Resampling Methods For Dependent Data
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Author : S. N. Lahiri
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-08-07
Resampling Methods For Dependent Data written by S. N. Lahiri 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 2003-08-07 with Mathematics categories.
By giving a detailed account of bootstrap methods and their properties for dependent data, this book provides illustrative numerical examples throughout. The book fills a gap in the literature covering research on re-sampling methods for dependent data that has witnessed vigorous growth over the last two decades but remains scattered in various statistics and econometrics journals. It can be used as a graduate level text and also as a research monograph for statisticians and econometricians.
Bootstrap Methods And Their Application
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Author : Anthony Christopher Davison
language : en
Publisher:
Release Date : 1997
Bootstrap Methods And Their Application written by Anthony Christopher Davison and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Bootstrap (Statistics) categories.
Statistical Intervals
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Author : William Q. Meeker
language : en
Publisher: John Wiley & Sons
Release Date : 2017-04-10
Statistical Intervals written by William Q. Meeker 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 2017-04-10 with Mathematics categories.
Describes statistical intervals to quantify sampling uncertainty,focusing on key application needs and recently developed methodology in an easy-to-apply format Statistical intervals provide invaluable tools for quantifying sampling uncertainty. The widely hailed first edition, published in 1991, described the use and construction of the most important statistical intervals. Particular emphasis was given to intervals—such as prediction intervals, tolerance intervals and confidence intervals on distribution quantiles—frequently needed in practice, but often neglected in introductory courses. Vastly improved computer capabilities over the past 25 years have resulted in an explosion of the tools readily available to analysts. This second edition—more than double the size of the first—adds these new methods in an easy-to-apply format. In addition to extensive updating of the original chapters, the second edition includes new chapters on: Likelihood-based statistical intervals Nonparametric bootstrap intervals Parametric bootstrap and other simulation-based intervals An introduction to Bayesian intervals Bayesian intervals for the popular binomial, Poisson and normal distributions Statistical intervals for Bayesian hierarchical models Advanced case studies, further illustrating the use of the newly described methods New technical appendices provide justification of the methods and pathways to extensions and further applications. A webpage directs readers to current readily accessible computer software and other useful information. Statistical Intervals: A Guide for Practitioners and Researchers, Second Edition is an up-to-date working guide and reference for all who analyze data, allowing them to quantify the uncertainty in their results using statistical intervals.