On the rate of convergence of the ECME algorithm for multiple regression models with t-distributed errors

Jeanne Kowalski, M. T U Xin, Roger S. Day, José R. Mendoza-Blanco

Research output: Contribution to journalArticle

Abstract

Although much work has been done on comparing and contrasting the EM and ECME algorithms, in terms of their rates of convergence, it is not clear what mechanism underlies each and, furthermore, what factors may determine and influence their rates of convergence. In this paper, we examine the convergence rates and properties of these two popular optimisation algorithms as used in computing the maximum likelihood estimates from regression models with {-distributed errors. By approaching this computing problem through the use of two data augmentation schemes, as well as variations of these wellknown algorithms, we offer a more composite view on the performance of each.

Original languageEnglish (US)
Pages (from-to)269-281
Number of pages13
JournalBiometrika
Volume84
Issue number2
Publication statusPublished - 1997
Externally publishedYes

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Keywords

  • ECME
  • EM
  • Maximum likelihood
  • R-distribution
  • Step-length newton's method

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Statistics and Probability
  • Mathematics(all)
  • Applied Mathematics

Cite this

Kowalski, J., Xin, M. T. U., Day, R. S., & Mendoza-Blanco, J. R. (1997). On the rate of convergence of the ECME algorithm for multiple regression models with t-distributed errors. Biometrika, 84(2), 269-281.