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2019 Circumventing superefficiency: An effective strategy for distributed computing in non-standard problems
Moulinath Banerjee, Cécile Durot
Electron. J. Statist. 13(1): 1926-1977 (2019). DOI: 10.1214/19-EJS1559


We propose a strategy for computing estimators in some non-standard M-estimation problems, where the data are distributed across different servers and the observations across servers, though independent, can come from heterogeneous sub-populations, thereby violating the identically distributed assumption. Our strategy fixes the super-efficiency phenomenon observed in prior work on distributed computing in (i) the isotonic regression framework, where averaging several isotonic estimates (each computed at a local server) on a central server produces super-efficient estimates that do not replicate the properties of the global isotonic estimator, i.e. the isotonic estimate that would be constructed by transferring all the data to a single server, and (ii) certain types of M-estimation problems involving optimization of discontinuous criterion functions where M-estimates converge at the cube-root rate. The new estimators proposed in this paper work by smoothing the data on each local server, communicating the smoothed summaries to the central server, and then solving a non-linear optimization problem at the central server. They are shown to replicate the asymptotic properties of the corresponding global estimators, and also overcome the super-efficiency phenomenon exhibited by existing estimators.


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Moulinath Banerjee. Cécile Durot. "Circumventing superefficiency: An effective strategy for distributed computing in non-standard problems." Electron. J. Statist. 13 (1) 1926 - 1977, 2019.


Received: 1 June 2018; Published: 2019
First available in Project Euclid: 19 June 2019

zbMATH: 07080065
MathSciNet: MR3964267
Digital Object Identifier: 10.1214/19-EJS1559

Primary: 62G05, 62G08, 62G20
Secondary: 62E20


Vol.13 • No. 1 • 2019
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