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This paper studies hypothesis testing and parameter estimation in the context of the divide-and-conquer algorithm. In a unified likelihood based framework, we propose new test statistics and point estimators obtained by aggregating various statistics from k subsamples of size n/k, where n is the sample size. In both low dimensional and sparse high dimensional settings, we address the important question of how large k can be, as n grows large, such that the loss of efficiency due to the divide-and-conquer algorithm is negligible. In other words, the resulting estimators have the same inferential efficiencies and estimation rates as an oracle with access to the full sample. Thorough numerical results are provided to back up the theory.

First we prove a new inequality comparing uniformly the relative volume of a Borel subset with respect to any given complex euclidean ball$B$⊂$C$^{$n$}with its relative logarithmic capacity in$C$^{$n$}with respect to the same ball$B$. An analogous comparison inequality for Borel subsets of euclidean balls of any generic real subspace of$C$^{$n$}is also proved.

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