Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10524545 | Journal of Multivariate Analysis | 2005 | 24 Pages |
Abstract
One of the most powerful algorithms for maximum likelihood estimation for many incomplete-data problems is the EM algorithm. The restricted EM algorithm for maximum likelihood estimation under linear restrictions on the parameters has been handled by Kim and Taylor (J. Amer. Statist. Assoc. 430 (1995) 708-716). This paper proposes an EM algorithm for maximum likelihood estimation under inequality restrictions A0β⩾0, where β is the parameter vector in a linear model W=Xβ+ε and ε is an error variable distributed normally with mean zero and a known or unknown variance matrix Σ>0. Some convergence properties of the EM sequence are discussed. Furthermore, we consider the consistency of the restricted EM estimator and a related testing problem.
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Ning-Zhong Shi, Shu-Rong Zheng, Jianhua Guo,