There are three SAS procedures that enable you to do maximum likelihood estimation of parameters in an arbitrary model with a likelihood function that you define: PROC MODEL, PROC NLP, and PROC IML.

and is the normal probability function. This is the likelihood function for a binary probit model. This likelihood is strictly positive so that you can take a square root of and use this as your ...

In this paper, parametric and empirical likelihood functions or surfaces are compared. In particular, first- and second-order expansions for log likelihood functions are developed in nonparametric and ...

The existence of maximum likelihood estimates for a class of heterocedastic regression models in considered. For a given dispersion function we show that, under a weak condition, the likelihood in ...

Mixed model analyses via restricted maximum likelihood, fitting the so-called animal model, have become standard methodology for the estimation of genetic variances. Models involving multiple genetic ...

The development of prediction models that included variables such as pretreatment sexual function, patient characteristics and treatment factors appear to be effective at predicting erectile function ...