$L^1$ bounds in normal approximation

Details $L^1$ bounds in normal approximation $L^1$ bounds in normal approximation

2007-10-17

arxiv.org/abs/0710.3262v1

Annals of Probability 2007, Vol. 35, No. 5, 1888-1930

Mathematics

Probability

Published in at http://dx.doi.org/10.1214/009117906000001123 the Annals of Probability (http://www.imstat.org/aop/) by the Ins

Scientific paper

10.1214/009117906000001123

The zero bias distribution $W^*$ of $W$, defined though the characterizing equation $\mathit{EW}f(W)=\sigma^2Ef'(W^*)$ for all smooth functions $f$, exists for all $W$ with mean zero and finite variance $\sigma^2$. For $W$ and $W^*$ defined on the same probability space, the $L^1$ distance between $F$, the distribution function of $W$ with $\mathit{EW}=0$ and $Var(W)=1$, and the cumulative standard normal $\Phi$ has the simple upper bound \[\Vert F-\Phi\Vert_1\le2E|W^*-W|.\] This inequality is used to provide explicit $L^1$ bounds with moderate-sized constants for independent sums, projections of cone measure on the sphere $S(\ell_n^p)$, simple random sampling and combinatorial central limit theorems.

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