A Bayesian approach to the analysis of a two-phase linear
regression model is given. It is assumed that the regression model is
continuous at the change point. The likelihood function is expressed
in a form which explicitly contains the continuity restriction. The
natural conjugate prior distribution for the likelihood function...
Mixed models have been widely used to model data from experiments which have fixed and random
factors. Often there is interest in the estimation of fixed effects and variance components. The likelihood
procedure is a general technique that has been applied to such problems. This procedure can be
computationally difficult,...
An iterative approach is suggested for the estimation
of the error covariance matrix [sigma] to find approximate BLUE
estimators in linear regression models.
It is shown through
experimental studies how the variances can be estimated in
the simple general linear regression model, in the linear
regression model sequenced over time,...
Data in the form of counts or proportions often exhibit more
variability than that predicted by a Poisson or binomial
distribution. Many different models have been proposed to account
for extra-Poisson or extra-binomial variation. A simple model
includes a single heterogeneity factor (dispersion parameter) in the
variance. Other models that...
