Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 13, Number 1 (2019), 1926-1977.
Circumventing superefficiency: An effective strategy for distributed computing in non-standard problems
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.
Electron. J. Statist., Volume 13, Number 1 (2019), 1926-1977.
Received: June 2018
First available in Project Euclid: 19 June 2019
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Banerjee, Moulinath; Durot, Cécile. Circumventing superefficiency: An effective strategy for distributed computing in non-standard problems. Electron. J. Statist. 13 (2019), no. 1, 1926--1977. doi:10.1214/19-EJS1559. https://projecteuclid.org/euclid.ejs/1560909646