Resampling Methods for Dependent Data: (Springer Series in Statistics)

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.

Table of Contents:
1 Scope of Resampling Methods for Dependent Data.- 2 Bootstrap Methods.- 3 Properties of Block Bootstrap Methods for the Sample Mean.- 4 Extensions and Examples.- 5 Comparison of Block Bootstrap Methods.- 6 Second-Order Properties.- 7 Empirical Choice of the Block Size.- 8 Model-Based Bootstrap.- 9 Frequency Domain Bootstrap.- 10 Long-Range Dependence.- 11 Bootstrapping Heavy-Tailed Data and Extremes.- 12 Resampling Methods for Spatial Data.- A.- B.- References.- Author Index.

Review :
From the reviews: "This book contains a large amount of material on resampling methods for dependent data a ] . the book is self-contained and therefore can be used as a text for a graduate level course in resampling methods; at the same time, the book is a valuable reference book for researchers. a ] This is a thorough book going into much detail a ] . an excellent book on resampling methods for dependent data which has filled a long lasting gap in the statistical literature." (Efstathios Paparoditis, Sankhya: The Indian Journal of Statistics, Vol. 65 (4), 2003) "I found this a useful book that organizes many scattered results in a reasonably concise form. The author states that this book has two main audiences, so the first five chapters are a pedantic introduction aimed at graduate students and the last seven a research monograph aimed at researchers in statistics and econometrics. a ] In summary, I learned quite a bit from reading this book and consider it a good reference book for the mathematically inclined." (D.J. Thomson, Short Book Reviews, Vol. 24 (2), 2004) "Bootstrap methods have seen vigorous growth over the past twenty years, and the book by Lahiri is extremely timely in its appearance. a ] The first five chapters are written in textbook style and this part is aimed at a postgraduate student audience. a ] The second part of the book (chapters 6 a" 12) is written in the form of a research monograph. It is therefore primarily aimed at researchers a ] . this is a well written book, containing a wealth of information a ] ." (Tertius de Wet, Newsletter of the South African Statistical Association, June, 2004) "This book is devoted to resampling methods fordependent data, which has been a fast developing area in about the last twenty years. a ] provides an introduction to the area of resampling methods for dependent data and also presents the latest results in the area with quite a long reference list. The first part of the book can be used as a textbook, while the second part, which focuses on the advanced results, can be really useful for researchers in statistics and econometrics." (M. HuAkovA, Mathematical Reviews, 2004f) From the reviews: "This book contains a large amount of material on resampling methods for dependent data ??? . the book is self-contained and therefore can be used as a text for a graduate level course in resampling methods; at the same time, the book is a valuable reference book for researchers. ??? This is a thorough book going into much detail ??? . an excellent book on resampling methods for dependent data which has filled a long lasting gap in the statistical literature." (Efstathios Paparoditis, Sankhya: The Indian Journal of Statistics, Vol. 65 (4), 2003) "I found this a useful book that organizes many scattered results in a reasonably concise form. The author states that this book has two main audiences, so the first five chapters are a pedantic introduction aimed at graduate students and the last seven a research monograph aimed at researchers in statistics and econometrics. ??? In summary, I learned quite a bit from reading this book and consider it a good reference book for the mathematically inclined." (D.J. Thomson, Short Book Reviews, Vol. 24 (2), 2004) "Bootstrap methods have seen vigorous growth over the past twenty years, and the book by Lahiri is extremely timely in its appearance. ??? The first five chapters are written in textbook style and this part is aimed at a postgraduate student audience. ??? The second part of the book (chapters 6 ??? 12) is written in the form of a research monograph. It is therefore primarily aimed at researchers ??? . this is a well written book, containing a wealth of information ??? ." (Tertius de Wet, Newsletter of the South African StatisticalAssociation, June, 2004) "This book is devoted to resampling methods for dependent data, which has been a fast developing area in about the last twenty years. ??? provides an introduction to the area of resampling methods for dependent data and also presents the latest results in the area with quite a long reference list. The first part of the book can be used as a textbook, while the second part, which focuses on the advanced results, can be really useful for researchers in statistics and econometrics." (M. Hu??kov??, Mathematical Reviews, 2004f)