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A weak Cramér condition and application to Edgeworth expansions. (English) Zbl 1380.60027
Summary: We introduce a new, weak Cramér condition on the characteristic function of a random vector which does not only hold for all continuous distributions but also for discrete (non-lattice) ones in a generic sense. We then prove that the normalized sum of independent random vectors satisfying this new condition automatically verifies some small ball estimates and admits a valid Edgeworth expansion for the Kolmogorov metric. The latter results therefore extend the well known theory of Edgeworth expansion under the standard Cramér condition, to distributions that are purely discrete.

MSC:
60E10 Characteristic functions; other transforms
60G50 Sums of independent random variables; random walks
62E20 Asymptotic distribution theory in statistics
62E17 Approximations to statistical distributions (nonasymptotic)
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