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Weak and strong representations for quantile processes from finite populations with application to simulation size in resampling inference
Author(s) -
Shi Xiquan,
Wu C. F. J.,
Chen Jiahua
Publication year - 1990
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315562
Subject(s) - resampling , quantile , inference , sample size determination , mathematics , monte carlo method , population , statistical inference , statistics , statistical physics , algorithm , computer science , physics , artificial intelligence , demography , sociology
We prove three results on the weak or strong representations for quantile processes of samples drawn randomly with or without replacement from a finite population. As an application of the strong‐approximation result (Theorem 2), we give an approach for determining the order of B , the number of Monte Carlo simulations required for the accuracy of resampling inference. In typical situations the order of B is between nb n and n 2+δ , where n is the original sample size, δ > 0, and (log log n )/ b n → 0.