Open AccessApproximation for bootstrapped empirical processes
Author(s) -
Miklós Csörgő,
Lajos Horváth,
Piotr Kokoszka
Publication year - 1999
Publication title -
proceedings of the american mathematical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.968
H-Index - 84
eISSN - 1088-6826
pISSN - 0002-9939
DOI - 10.1090/s0002-9939-99-05409-x
Subject(s) - poisson distribution , gaussian process , approximation theory , mathematics , empirical distribution function , high frequency approximation , gaussian , function approximation , spouge's approximation , minimax approximation algorithm , statistical physics , statistics , mathematical analysis , computer science , physics , quantum mechanics , artificial intelligence , artificial neural network , scattering
We obtain an approximation for the bootstrapped empirical process with the rate of the Komlós, Major and Tusnády approximation for empirical processes. The proof of the new approximation is based on the Poisson approximation for the uniform empirical distribution function and the Gaussian approximation for randomly stopped sums.