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International Journal of Mathematics and Mathematical Sciences
Article . 1991 . Peer-reviewed
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Strong consistencies of the bootstrap moments

Authors: Tien-Chung Hu;

Strong consistencies of the bootstrap moments

Abstract

Let X be a real valued random variable with E|X|r+δ < ∞ for some positive integer r and real number, δ, 0 < δ ≤ r, and let {X, X1, X2, …} be a sequence of independent, identically distributed random variables. In this note, we prove that, for almost all w ∈ Ω, with probability 1. if for some , where is the bootstrap rth sample moment of the bootstrap sample some with sample size m(n) from the data set {X, X1, …, Xn} and μr is the rth moment of X. The results obtained here not only improve on those of Athreya [3] but also the proof is more elementary.

Keywords

Strong limit theorems, strong law of large numbers, Asymptotic distribution theory in statistics, bootstrap sample size, convergence with probability 1., convergence with probability 1, Asymptotic properties of nonparametric inference, sample moment, QA1-939, Nonparametric statistical resampling methods, SLLN, Mathematics

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
gold