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On the Glivenko–Cantelli theorem for generalized empirical processes based on strong mixing sequences
Authors:Y S Rama Krishnaiah
Institution:

Department of Statistics and Applied Probability, University of Alberta, Edmonton, Canada T6G 2G1

Abstract:Given \s{Xi, i greater-or-equal, slanted 1\s} as non-stationary strong mixing (n.s.s.m.) sequence of random variables (r.v.'s) let, for 1 less-than-or-equals, slant i less-than-or-equals, slant n and some γ ε 0, 1],
F1(x)=γP(Xi<x)+(1-γ)P(Xiless-than-or-equals, slantx)
and
Ii(x)=γI(Xi<x)+(1-γ)I(Xiless-than-or-equals, slantx)
. For any real sequence \s{Ci\s} satisfying certain conditions, let
Image
.

In this paper an exponential type of bound for P(Dn var epsilon), for any var epsilon >0, and a rate for the almost sure convergence of Dn are obtained under strong mixing. These results generalize those of Singh (1975) for the independent and non-identically distributed sequence of r.v.'s to the case of strong mixing.

Keywords:Generalized empirical processes  strong mixing  almost sure convergence
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