A massive data framework for M-estimators with cubic-rate

Chengchun Shi, Wenbin Lu and Rui Song

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: The divide and conquer method is a common strategy for handling massive data. In this article, we study the divide and conquer method for cubic-rate estimators under the massive data framework. We develop a general theory for establishing the asymptotic distribution of the aggregated M-estimators using a weighted average with weights depending on the subgroup sample sizes. Under certain condition on the growing rate of the number of subgroups, the resulting aggregated estimators are shown to have faster convergence rate and asymptotic normal distribution, which are more tractable in both computation and inference than the original M-estimators based on pooled data. Our theory applies to a wide class of M-estimators with cube root convergence rate, including the location estimator, maximum score estimator, and value search estimator. Empirical performance via simulations and a real data application also validate our theoretical findings. Supplementary materials for this article are available online.

Keywords: cubic rate asymptotics; divide and conquer; M-estimators; massive data (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 12 pages
Date: 2018-10-02
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Published in Journal of the American Statistical Association, 2, October, 2018, 113(524), pp. 1698 - 1709. ISSN: 0162-1459

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:102111

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