CML

Constrained Maximum Likelihood Estimation

CML is a module written in the GAUSS programming language. It solves the general maximum likelihood problem subject to general constraints on the parameters -- linear or nonlinear, equality or inequality. CML uses the Sequential Quadratic Programming method in combination with several descent methods selectable by the user -- Newton-Raphson, BFGS, DFP, or BHHH. There are also several selectable line search methods. Gradients can be user-provided or numerically calculated.

CML provides for statistical inference for constrained statistical models. Confidence limits may be computed from selected methods, bootstrap, Bayesian (using a weighted likelihood bootstrap), or inversion of three types of statistics, the Wald, the likelihood ratio, or the Lagrange Multiplier. Confidence limits from the inversion of the likelihood ratio statistic are also called profile likelihood confidence limits.

The bootstrap and Bayesian procedures generate simulated parameter sets from the bootstrap and posterior distributions respectively. Procedures may be applied to these parameter sets to either produce confidence limits, expected values, or kernel density plots of the distributions

CML comes as source code and requires the GAUSS programming language software. It is available for Windows NT, Windows 95, OS/2, DOS, and major UNIX platforms.

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