Dimitris N. Politis, Joseph P. Romano, Michael Wolf

Subsampling (eBook, PDF)

• Format: PDF

Produktbeschreibung

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. This book sets out to lay some of the foundations for subsampling methodology and related methods.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 348
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461215547
• Artikelnr.: 44178969

Autorenporträt

Joseph P. Romano is Professor of Statistics at Stanford University. He is a recipient of a Presidential Young Investigator Award and a Fellow of the Institute of Mathematical 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.

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