The Multivariate Central Limit Theorem for Regular Convex Sets
Matthes, T. K.
Ann. Probab., Tome 3 (1975) no. 6, p. 503-515 / Harvested from Project Euclid
Let $X_1, X_2, \cdots$ be i.i.d. random vectors in $R_k$. Let $P_n$ denote the probability measure induced by the normalized sum and let $Q_n$ denote the multivariate Edgeworth signed measure with terms through $n^{-\frac{1}{2}}$. If $C$ is a member of a class of convex bodies whose boundaries are sufficiently smooth and possess positive Gaussian curvatures, and $X_1$ has fourth moments, it is shown that $P_n(C) - Q_n(C) = 0(n^{-k/(k + 1)})$ where the bound is uniform. If, moreover, $X_1$ has a nonlattice distribution, the difference is $o(n^{-k/(k + 1)})$.
Publié le : 1975-06-14
Classification:  Multivariate central limit theorem,  Edgeworth expansion,  rates of convergence,  probability of convex sets,  nonlattice distributions,  62E20,  60F05,  60B15
@article{1176996356,
     author = {Matthes, T. K.},
     title = {The Multivariate Central Limit Theorem for Regular Convex Sets},
     journal = {Ann. Probab.},
     volume = {3},
     number = {6},
     year = {1975},
     pages = { 503-515},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1176996356}
}
Matthes, T. K. The Multivariate Central Limit Theorem for Regular Convex Sets. Ann. Probab., Tome 3 (1975) no. 6, pp.  503-515. http://gdmltest.u-ga.fr/item/1176996356/