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.

Maximum likelihood estimation of the parameters of a statistical model involves maximizing the likelihood or, equivalently, the log likelihood with respect to the parameters. The parameter values at ...

Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

This is a preview. Log in through your library . Abstract In a linear (or affine) functional model the principal parameter is a subspace (respectively an affine subspace) in a finite dimensional inner ...

This is a preview. Log in through your library . Abstract We focus on a class of non-standard problems involving non-parametric estimation of a monotone function that is characterized by n1/3 rate of ...

The challenge of using small sample sizes for operational risk capital models fitted via maximum likelihood estimation is well recognized, yet the literature generally provides warning examples rather ...