Since Efron's profound paper on the bootstrap, an enormous amount of effort has been spent on the development of bootstrap, jacknife, and other resampling methods. The primary goal of these computer-intensive methods has been to provide statistical tools that work in complex situations without imposing unrealistic or unverifiable assumptions about the data generating mechanism. The primary goal of this book is to lay some of the foundation for subsampling methodology and related methods.
Since Efron's profound paper on the bootstrap, an enormous amount of effort has been spent on the development of bootstrap, jacknife, and other resampling methods. The primary goal of these computer-intensive methods has been to provide statistical tools that work in complex situations without imposing unrealistic or unverifiable assumptions about the data generating mechanism. The primary goal of this book is to lay some of the foundation for subsampling methodology and related methods.
Dimitris N. Politis is Professor of Mathematics and Adjunct Professor of Economics at the University of California, San Diego. His research interests include Time Series Analysis, Resampling and Subsampling, Nonparametric Function Estimation, and Model-free Prediction. He has served as Editor of the IMS Bulletin (2010-2013), Co-Editor of the Journal of Time Series Analysis (2013-present), Co-Editor of the Journal of Nonparametric Statistics (2008-2011), and as Associate Editor for several journals including Bernoulli, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society, Series B. He is a fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association, former fellow of the John Simon Guggenheim Memorial Foundation, and co-founder (with M. Akritas and S.N. Lahiri) of the International Society for NonParametric Statistics.
Inhaltsangabe
I Basic Theory.- 1 Bootstrap Sampling Distributions.- 2 Subsampling in the I.I.D. Case.- 3 Subsampling for Stationary Time Series.- 4 Subsampling for Nonstationary Time Series.- 5 Subsampling for Random Fields.- 6 Subsampling Marked Point Processes.- 7 Confidence Sets for General Parameters.- II Extensions, Practical Issues, and Applications.- 8 Subsampling with Unknown Convergence Rate.- 9 Choice of the Block Size.- 10 Extrapolation, Interpolation, and Higher-Order Accuracy.- 11 Subsampling the Mean with Heavy Tails.- 12 Subsampling the Autoregressive Parameter.- 13 Subsampling Stock Returns.- Appendices.- A Some Results on Mixing.- B A General Central Limit Theorem.- References.- Index of Names.- Index of Subjects.
I Basic Theory.- 1 Bootstrap Sampling Distributions.- 2 Subsampling in the I.I.D. Case.- 3 Subsampling for Stationary Time Series.- 4 Subsampling for Nonstationary Time Series.- 5 Subsampling for Random Fields.- 6 Subsampling Marked Point Processes.- 7 Confidence Sets for General Parameters.- II Extensions, Practical Issues, and Applications.- 8 Subsampling with Unknown Convergence Rate.- 9 Choice of the Block Size.- 10 Extrapolation, Interpolation, and Higher-Order Accuracy.- 11 Subsampling the Mean with Heavy Tails.- 12 Subsampling the Autoregressive Parameter.- 13 Subsampling Stock Returns.- Appendices.- A Some Results on Mixing.- B A General Central Limit Theorem.- References.- Index of Names.- Index of Subjects.
Rezensionen
"The book is one of the most comprehensive texts in the subsampling realm and provides a solid background for researchers working in the related areas of statistics." V.V. Fedorov in "Short Book Reviews", Vol. 21/1, April 2001
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