MAXLIK

Unconstrained Maximum Likelihood Estimation

MAXLIK is a module written in the GAUSS programming language It solves the general maximum likelihood problem. It uses several descent methods selectable by the user - Newton-Raphson, BFGS, DFP, BHHH, or Polak-Ribiere conjugate gradient method. There are also several selectable line search methods. Gradients can be user-provided or numerically calculated.

MAXLIK provides for statistical inference for maximum likelihood estimates. The usual Wald standard errors computed from the inversion of the Hessian of the log-likelihood is the default method. Confidence limits may also be computed from selected methods, bootstrap, Bayesian (using a weighted likelihood bootstrap), or inversion of the the likelihood ratio or the Lagrange Multiplier statistics. 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 value, or kernel density plots of the distributions

MAXLIK 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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