A Block Minorization-Maximization Algorithm for Heteroscedastic Regression

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-ma...

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Bibliographic Details
Published in:	IEEE signal processing letters Vol. 23; no. 8; pp. 1131 - 1135
Main Authors:	Nguyen, Hien D., Lloyd-Jones, Luke R., McLachlan, Geoffrey J.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis Algorithms Big data Biological system modeling Computation Computational modeling Computer simulation Convergence Estimators Heteroscedastic regression Inversions Mathematical analysis Mathematical models maximum likelihood (ML) estimation Maximum likelihood estimation minorization–maximization (MM) algorithm parallel algorithm Regression Signal processing algorithms
ISSN:	1070-9908, 1558-2361
Online Access:	Get full text
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