Non-Uniform Bounds of Normal Approximation for Finite-Population U-Statistics.

1985 ◽  
Author(s):  
M. Baiqi ◽  
Z. Lincheng
Keyword(s):  
Finite Population ◽  
Normal Approximation ◽  
Uniform Bounds ◽  
U Statistics

scholarly journals Normal Approximation of U-Statistics in Hilbert Space

1997 ◽  
Vol 41 (3) ◽  
pp. 405-424 ◽  
Author(s):  
Yu. V. Borovskikh ◽  
L. Madan Puri ◽  
V. V. Sazonov
Keyword(s):  
Hilbert Space ◽  
Normal Approximation ◽  
U Statistics

scholarly journals Bootstrap, jackknife and Edgeworth approximations for finite population L-statistics

2010 ◽  
Vol 51 ◽  
Author(s):  
Andrius Čiginas
Keyword(s):  
Distribution Function ◽  
Edgeworth Expansion ◽  
Finite Population ◽  
Normal Approximation ◽  
Bootstrap Approximation ◽  
True Distribution

In this paper we give exact bootstrap estimators for the parameters defining one-term Edgeworth expansion of distribution function of finite population L-statistic and compare these estimators with corresponding jackknife estimators. We also compare `````` true’ distribution of L-statistic with its normal approximation, Edgeworth expansion, empirical Edgeworth expansion and bootstrap approximation.

scholarly journals An Edgeworth Expansion for Finite-Population U-Statistics

Bernoulli ◽  
2000 ◽  
Vol 6 (4) ◽  
pp. 729 ◽  
Author(s):  
Mindaugas Bloznelis ◽  
Friedrich Götze ◽  
Friedrich Gotze
Keyword(s):  
Edgeworth Expansion ◽  
Finite Population ◽  
U Statistics

scholarly journals Rates of strong convergence for U-statistics in finite populations

1991 ◽  
Vol 50 (3) ◽  
pp. 468-480
Author(s):  
P. N. Kokic ◽  
N. C. Weber
Keyword(s):  
Strong Convergence ◽  
Random Sample ◽  
Finite Population ◽  
Random Variables ◽  
Rates Of Convergence ◽  
Simple Random Sample ◽  
Finite Populations ◽  
Strong Law ◽  
U Statistics ◽  
Convergence Results

AbstractLet UNn be a U-statistic based on a simple random sample of size n selected without replacement from a finite population of size N. Rates of convergence results in the strong law are obtained for UNn, which are similar to those known for classical U-statistics based on samples of independent and identically distributed (iid) random variables.

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