Abstract

Let ~$\veps_1, ..., \veps_m$ be i.i.d. random variables with $$P(\veps_i=1)= P(\veps_i= -1)=1/2,$$ and $X_m = \sum_{i=1}^m \veps_i.$ Let YmY_m be a normal random variable with the same first two moments as that of Xm.X_m. There is a uniquely determined function ΨmΨ_m such that the distribution of Ψm(Ym)Ψ_m(Y_m) equals to the distribution of XmX_m. Tusnády's inequality states that ∣Ψm(Ym)−Ym∣≤Ym2m+1. \mid Ψ_m(Y_m) - Y_m \mid \leq \frac{Y_m^2}{m}+1. Here we propose a sharpened version of this inequality.

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