Constrained maximum likelihood estimation of two-level covariance structure model via EM type algorithms

Psychometrika ◽  
1999 ◽  
Vol 64 (4) ◽  
pp. 435-450 ◽  
Author(s):  
Sik-Yum Lee ◽  
Sin-Yu Tsang
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Covariance Structure ◽  
Likelihood Estimation ◽  
Structure Model ◽  
Covariance Structure Model ◽  
Constrained Maximum Likelihood Estimation ◽  
Constrained Maximum Likelihood

scholarly journals Numerical algorithms for constrained maximum likelihood estimation

2003 ◽  
Vol 45 (1) ◽  
pp. 91-114 ◽  
Author(s):  
Z. F. Li ◽  
M. R. Osborne ◽  
T. Prvan
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Information Matrix ◽  
Numerical Algorithms ◽  
Trust Region ◽  
Likelihood Estimation ◽  
Quadratic Model ◽  
Mixture Density ◽  
Constrained Maximum Likelihood Estimation ◽  
Constrained Maximum Likelihood

AbstractThis paper describes a SQP-type algorithm for solving a constrained maximum likelihood estimation problem that incorporates a number of novel features. We call it MLESOL. MLESOL maintains the use of an estimate of the Fisher information matrix to the Hessian of the negative log-likelihood but also encompasses a secant approximation S to the second-order part of the augmented Lagrangian function along with tests for when to use this information. The local quadratic model used has a form something like that of Tapia's SQP augmented scale BFGS secant method but explores the additional structure of the objective function. The step choice algorithm is based on minimising a local quadratic model subject to the linearised constraints and an elliptical trust region centred at the current approximate minimiser. This is accomplished using the Byrd and Omojokun trust region approach, together with a special module for assessing the quality of the step thus computed. The numerical performance of MLESOL is studied by means of an example involving the estimation of a mixture density.

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