Concentration inequalities, large and moderate deviations for self-normalized empirical processes

被引:0
|
作者
Bercu, B
Gassiat, E
Rio, E
机构
[1] Univ Paris 11, CNRS, Math Lab, UMR 8628, F-91405 Orsay, France
[2] CNRS, Math Lab, UMR 8100, F-78035 Versailles, France
来源
ANNALS OF PROBABILITY | 2002年 / 30卷 / 04期
关键词
maximal inequalities; self-normalized sums; empirical processes; concentration inequalities; large deviations; moderate deviations; logarithmic Sobolev inequalities;
D O I
暂无
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We consider the supremum W-n of self-normalized empirical processes indexed by unbounded classes of functions F. Such variables are of interest in various statistical applications, for example, the likelihood ratio tests of contamination. Using the Herbst method, we prove an exponential concentration inequality for W-n under a second moment assumption on the envelope function of F. This inequality is applied to obtain moderate deviations for W-n. We also provide large deviations results for some unbounded parametric classes F.
引用
收藏
页码:1576 / 1604
页数:29
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