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Estimation of the Distribution Function of a Standardized Statistic
Author(s) -
Lee Stephen M. S.,
Young G. Alastair
Publication year - 1997
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00074
Subject(s) - resampling , statistic , monte carlo method , minimax , mathematics , rate of convergence , convergence (economics) , statistics , bootstrap aggregating , computer science , mathematical optimization , key (lock) , computer security , economics , economic growth
For estimating the distribution of a standardized statistic, the bootstrap estimate is known to be local asymptotic minimax. Various computational techniques have been developed to improve on the simulation efficiency of uniform resampling, the standard Monte Carlo approach to approximating the bootstrap estimate. Two new approaches are proposed which give accurate yet simple approximations to the bootstrap estimate. The second of the approaches even improves the convergence rate of the simulation error. A simulation study examines the performance of these two approaches in comparison with other modified bootstrap estimates.