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Field name	Details
Dewey Class	519.5
Title	Resampling Methods for Dependent Data ([EBook]) / by S. N. Lahiri.
Author	Lahiri, S. N. (Soumendra Nath)
Other name(s)	SpringerLink (Online service)
Publication	New York, NY : Springer , 2003.
Physical Details	XIV, 374 pages : online resource.
Series	Springer Series in Statistics 0172-7397
ISBN	9781475738032
Summary Note	This is a book on bootstrap and related resampling methods for temporal and spatial data exhibiting various forms of dependence. Like the resam pling methods for independent data, these methods provide tools for sta tistical analysis of dependent data without requiring stringent structural assumptions. This is an important aspect of the resampling methods in the dependent case, as the problem of model misspecification is more preva lent under dependence and traditional statistical methods are often very sensitive to deviations from model assumptions. Following the tremendous success of Efron's (1979) bootstrap to provide answers to many complex problems involving independent data and following Singh's (1981) example on the inadequacy of the method under dependence, there have been several attempts in the literature to extend the bootstrap method to the dependent case. A breakthrough was achieved when resampling of single observations was replaced with block resampling, an idea that was put forward by Hall (1985), Carlstein (1986), Kiinsch (1989), Liu and Singh (1992), and others in various forms and in different inference problems. There has been a vig orous development in the area of res amp ling methods for dependent data since then and it is still an area of active research. This book describes various aspects of the theory and methodology of resampling methods for dependent data developed over the last two decades. There are mainly two target audiences for the book, with the level of exposition of the relevant parts tailored to each audience.:
Contents note	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.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site	http://dx.doi.org/10.1007/978-1-4757-3803-2
Links to Related Works	Subject References: Resampling (Statistics) . Resampling (Statistics) -- Statistical Theory and Methods . Statistics . Authors: Lahiri, S. N. (Soumendra Nath) . Lahiri, S. N. (Soumendra Nath) . Corporate Authors: SpringerLink (Online service) . Series: Springer Series in Statistics . Classification: 519.5 .

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Resampling Methods for Dependent Data

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