Resampling methods for dependent data

Soumen N. Lahiri - Collection Springer Series In Statistics

388 pages, parution le 08/09/2003

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Résumé

This book gives a detailed account of bootstrap methods and their properties for dependent data, covering a wide range of topics such as block bootstrap methods, bootstrap methods in the frequency domain, resampling methods for long range dependent data, and resampling methods for spatial data. The first five chapters of the book treat the theory and applications of block bootstrap methods at the level of a graduate text. The rest of the book is written as a research monograph, with frequent references to the literature, but mostly at a level accessible to graduate students familiar with basic concepts in statistics. Supplemental background material is added in the discussion of such important issues as second order properties of bootstrap methods, bootstrap under long range dependence, and bootstrap for extremes and heavy tailed dependent data. Further, illustrative numerical examples are given all through the book and issues involving application of the methodology are discussed. The book fills a gap in the literature covering research on resampling 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 for a special topics course on resampling methods for dependent data and also as a research monograph for statisticians and econometricians who want to learn more about the topic and want to apply the methods in their own research.

S.N. Lahiri is a professor of Statistics at the Iowa State University, is a Fellow of the Institute of Mathematical Statistics and a Fellow of the American Statistical Association.

Sommaire

Scope of Resampling Methods for Dependent Data
Bootstrap Methods
Properties of Block Bootstrap Methods for the Sample Mean
Extensions and Examples
Comparison of Block Bootstrap Methods
Second-Order Properties
Empirical Choice of the Block Size
Model-Based Bootstrap
Frequency Domain Bootstrap
Long-Range Dependence
Bootstrapping Heavy-Tailed Data and Extremes
Resampling Methods for Spatial Data

References
Index

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Soumen N. Lahiri
Collection	Springer Series In Statistics
Parution	08/09/2003
Nb. de pages	388
Format	16 x 24
Couverture	Relié
Poids	670g
Intérieur	Noir et Blanc
EAN13	9780387009285
ISBN13	978-0-387-00928-5