Statistics and Computing

, Volume 12, Issue 3, pp 281–285

Least absolute deviations estimation via the EM algorithm

  • Robert F. Phillips

DOI: 10.1023/A:1020759012226

Cite this article as:
Phillips, R.F. Statistics and Computing (2002) 12: 281. doi:10.1023/A:1020759012226


This paper derives EM and generalized EM (GEM) algorithms for calculating least absolute deviations (LAD) estimates of the parameters of linear and nonlinear regression models. It shows that Schlossmacher's iterative reweighted least squares algorithm for calculating LAD estimates (E.J. Schlossmacher, Journal of the American Statistical Association 68: 857–859, 1973) is an EM algorithm. A GEM algorithm for computing LAD estimates of the parameters of nonlinear regression models is also provided and is applied in some examples.

iterative reweighted least squares double exponential distribution nonlinear regression normal mixture 

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Robert F. Phillips

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