S. N. Lahiri

Resampling Methods for Dependent Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 374
• Erscheinungstermin: 9. März 2013
• Englisch
• ISBN-13: 9781475738032
• Artikelnr.: 44053900

Autorenporträt

S.N. Lahiri, Iowa State University, Ames, IA, USA

Inhaltsangabe

From the contents:

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.- Model Based Bootstrap.- Frequency Domain Bootstrap.- Long Range Dependence.- Resampling Methods for Spatial Data.- Special Topics.

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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.