A block minorization-maximization algorithm for heteroscedastic regression

Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016) A block minorization-maximization algorithm for heteroscedastic regression. IEEE Signal Processing Letters, 23 8: 1131-1135. doi:10.1109/LSP.2016.2586180

Author Nguyen, Hien D.
Lloyd-Jones, Luke R.
McLachlan, Geoffrey J.
Title A block minorization-maximization algorithm for heteroscedastic regression
Journal name IEEE Signal Processing Letters   Check publisher's open access policy
ISSN 1070-9908
Publication date 2016-08-01
Year available 2016
Sub-type Article (original research)
DOI 10.1109/LSP.2016.2586180
Open Access Status Not yet assessed
Volume 23
Issue 8
Start page 1131
End page 1135
Total pages 5
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Abstract The computation of the maximum likelihood (ML) estimator for heteroscedastic regression models is considered. The traditional Newton algorithms for the problem require matrix multiplications and inversions, which are bottlenecks in modern Big Data contexts. A new Big Data-appropriate minorization-maximization (MM) algorithm is considered for the computation of the ML estimator. The MM algorithm is proved to generate monotonically increasing sequences of likelihood values and to be convergent to a stationary point of the log-likelihood function. A distributed and parallel implementation of the MM algorithm is presented, and the MM algorithm is shown to have differing time complexity to the Newton algorithm. Simulation studies demonstrate that the MM algorithm improves upon the computation time of the Newton algorithm in some practical scenarios where the number of observations is large.
Keyword Heteroscedastic regression
Maximum likelihood (ML) estimation
Minorization-maximization (MM) algorithm
Parallel algorithm
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
Centre for Advanced Imaging Publications
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