The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form, which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration…

A general latent variable model allows for maximum likelihood estimation with missing data. LISREL and LISCOMP programs may be used to carry out this estimation. Simulated data were generated. The proposed Full, Quasi-Likelihood estimator was found to be superior to listwise present quasi-likelihood and pairwise present approaches. (Author/GDC)

Comparisons are made between BILOG version 2.2 and LOGIST 5.0 version 2.5 in estimating the item parameters, traits, item characteristic functions, and test characteristic functions for the three-parameter logistic model. Speed and accuracy are reported for a number of 10, 20, and 40-item tests. (Author/GDC)

A variety of paired comparison, triple comparison, and ranking experiments are discussed as generalized linear models. All such models can be easily fit by maximum likelihood using the GLIM computer package. Examples are presented for a variety of cases using GLIM. (SLD)