Paper Info
Title
Weighted jackknife-after-bootstrap: a heuristic approach
Abstract
ABSTRACT We investigate the problem of deriving precision es - timates for bootstrap quantities The one major stipulation is that no further bootstrapping will be allowed In 1992, Efron derived the method of jackknife - after - bootstrap (JAB) and showed how this problem can potentially be solved However, the ap - plicability of JAB was questioned in situations where the number of bootstrap samples was not large The JAB estimates were inflated and performed poorly We provide a simple correction to the JAB method using a weighted form where the weights are derived from the original bootstrap samples Our Monte Carlo experiments show that the weighted jackknife - after - bootstrap (WJAB) performs very well
Year
DOI
Venue
1997
10.1145/268437.268486
Winter Simulation Conference
Keywords
Field
DocType
heuristic approach,weighted jackknife-after-bootstrap,computer science,statistical distributions,sampling methods,mathematics,statistics,probability,monte carlo methods
Monte Carlo method in statistical physics,Applied mathematics,Monte Carlo method,Jackknife resampling,Bootstrapping,Simulation,Computer science,Hybrid Monte Carlo,Probability distribution,Monte Carlo integration,Statistics,Bootstrapping (electronics)
Conference
ISBN
Citations 
PageRank 
0-7803-4278-X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Jin Wang183.50
J. Sunil Rao2364.19
Jun Shao321.86