
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.
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
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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