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 ...

This paper offers a Bayesian framework for the calibration of financial models using neural stochastic differential equations ...