The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Communication-Efficient Distributed Statistical Inference
Abstract
We present a communication-efficient surrogate likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation, and Bayesian inference. For low-dimensional estimation, CSL provably improves upon naive averaging schemes and facilitates the construction of confidence intervals. For high-dimensional regularized estimation, CSL leads to a minimax-optimal estimator with controlled communication cost. For Bayesian inference, CSL can be used to form a communication-efficient quasi-posterior distribution that converges to the true posterior. This quasi-posterior procedure significantly improves the computational efficiency of Markov chain Monte Carlo (MCMC) algorithms even in a nondistributed setting. We present both theoretical analysis and experiments to explore the properties of the CSL approximation. Supplementary materials for this article are available online.
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Citations
Cites
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method
Shamir, O., Srebro, N., & Zhang, T. (2013) · arXiv · doi:10.48550/arxiv.1312.7853 · Cited by 424
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Metadata
- Authors
- Jordan, M. I., Lee, J. D., & Yang, Y.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2018-11-13
- Volume
- 114
- Issue
- 526
- Pages
- 668-681
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2964231067
- Cited by
- 442
- References
- 1
- FWCI
- 23.99
- Type
- journal article
- Language
- en
- Open access
- closed
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