A parametric version of jackknife-after-bootstrap

Author

Wang, Jin

Author_Institution

Dept. of Math. & Comput. Sci., Valdosta State Univ., GA, USA

Volume

1

fYear

1998

fDate

13-16 Dec 1998

Firstpage

587

Abstract

We investigate the problem of deriving precision estimates for bootstrap quantities within parametric families. B. Efron´s (1992) jackknife-after-bootstrap is a simple approach that only uses the information in the original bootstrap samples via the importance sampling technique, with no further resampling required. This method can be applied to many Monte Carlo experiments, especially, to the parametric input modeling problems. Variance analysis of the parametric jackknife-after-bootstrap is discussed. Under some reasonable conditions, the parametric jackknife-after-bootstrap method is as good as the true jackknife method. A generalized parametric jackknife-after-bootstrap method is introduced

Keywords

importance sampling; parameter estimation; simulation; Monte Carlo experiments; bootstrap quantities; bootstrap samples; generalized parametric method; importance sampling technique; jackknife method; jackknife-after-bootstrap method; parametric families; parametric input modeling problems; parametric version; precision estimates; variance analysis; Analysis of variance; Application software; Computational modeling; Computer errors; Computer science; Mathematical model; Mathematics; Monte Carlo methods; Parametric statistics; Power system modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Simulation Conference Proceedings, 1998. Winter

Conference_Location

Washington, DC

Print_ISBN

0-7803-5133-9

Type

conf

DOI

10.1109/WSC.1998.745038

Filename

745038