We consider the problem of attrition under a logistic regression model for longitudinal binary data in which each subject has his own intercept parameter, and where parameters are eliminated via conditional logistic regression. This is a fixed-effects, subject-specific model which exploits the longitudinal data by allowing subjects to act as their own controls. By modeling and conditioning on t...

We consider the problem of attrition under a logistic regression model for longitudinal binary data in which each subject has his own intercept parameter, and those parameters are eliminated via conditional logistic regression. This is a fixed-effects, subject-specific model which exploits the longitudinal data by allowing subjects to act as their own controls. By modeling and conditioning on t...

The analysis of data generated by animal habitat selection studies, by family studies of genetic diseases, or by longitudinal follow-up of households often involves fitting a mixed conditional logistic regression model to longitudinal data composed of clusters of matched case-control strata. The estimation of model parameters by maximum likelihood is especially difficult when the number of case...

Sophisticated signal processing techniques have to be developed for capacity enhancement of future wireless communication systems. In recent years, space-time coding is proposed to provide significant capacity gains over the traditional communication systems in fading wireless channels. Space-time codes are obtained by combining channel coding, modulation, transmit diversity, and optional recei...

Michael D. Perlman-It is shown that each multivariate normal model determined by lattice conditional independence (LeI) restrictions on the covariances may be extended in a, natural way to a normal linear model with corresponding lattice restrictions on the means. For these extended models it remains true that the likelihood function (LF) and parameter space (PS) can be factored into the produc...

Maximum Likelihood (ML) estimation is extensively used for estimating emission densities from clumped and incomplete nzeasurement data in Positron Emission Tomography (PEU modality. Reconstruction produced by ML-algorithm has been found noisy because it does not make use of available prior knowledge. Bayesian estimation provides such a platform for the inclusion of prior knowledge in the recons...