Titlebook: Subsampling; Dimitris N. Politis,Joseph P. Romano,Michael Wolf Textbook 1999 Springer Science+Business Media New York 1999 Resampling.Subs

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书目名称Subsampling影响因子(影响力)




书目名称Subsampling影响因子(影响力)学科排名




书目名称Subsampling网络公开度




书目名称Subsampling网络公开度学科排名




书目名称Subsampling被引频次




书目名称Subsampling被引频次学科排名




书目名称Subsampling年度引用




书目名称Subsampling年度引用学科排名




书目名称Subsampling读者反馈




书目名称Subsampling读者反馈学科排名

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Subsampling in the I.I.D. Caselitis and Romano (1992c, 1994b). The basic idea is to approximate the sampling distribution of a statistic based on the values of the statistic computed over smaller subsets of the data. For example, in the case where the data are . observations that are independent and identically distributed, a st

dyspareunia

Subsampling for Nonstationary Time Seriesodeling real-life data. To mention only two examples, many economic time series exhibit seasonal fluctuations, while stock return data typically show time-dependent variability. The goal of this chapter is to demonstrate that the subsampling method is by no means restricted to stationary series. We

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Confidence Sets for General Parametersproblem we consider is inference for a parameter θ(.). The focus of the present chapter is the case when the parameter space Θ is a metric space. The reason for considering such generality is to be able to consider the case when the parameter of interest is an unknown function, such as the marginal

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Extrapolation, Interpolation, and Higher-Order Accuracyeasure generating the observations is again denoted by .. As mentioned in Appendix A, the strong mixing condition amounts to α. (.) =sup. |.(.⋂.) —.(.).(.)|→0 as . tends to infinity, where . and . are events in the σ-algebras generated by {. < 0 } and {., . ≥ .}, respectively. The case where .,…, .

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