NOTE: Maximum Likelihood has been replaced Maximum Likelihood MT. New code should be written using MaxLikMT.
MAXLIK performs maximum likelihood estimation of the parameters of statistical models. All you provide is a GAUSS function to calculate the log-likelihood for a set of observations. MAXLIK does the rest.
Major Features of Maximum Likelihood
- More than 25 user-selectable options control the optimization
- Fast Procedures: FASTMAX, FASTBoot, FASTBayes, FASTProfile, and FASTPflCLimits can speed convergence times up to 800 percent over earlier versions of MAXLIK, depending on the type of problem.
- “Kiss-Monster” random numbers used in the bootstrap procedure and random line search algorithm.
- The bootstrap and random line search procedures use the new “Kiss-Monster” random number generator. It has a period of 10^8859, long enough for serious Monte Carlo work.
- Descent algorithms include: BFGS (Broyden-Fletcher-Goldfarb-Shanno), DFP (Davidon-Fletcher-Powell), Newton, steepest descent, PRCG (Polak-Ribiere-type conjugate gradient), and BHHH (Berndt-Hall-Hall-Hausman)
- Step-length methods include: STEPBT, BRENT, BHHHSTEP, and a step-halving method
- A “switching” method may also be selected which switches the algorithm during the iterations according to three criteria: number of iterations, failure of the function to decrease within a tolerance, or decrease of the line search step length below a tolerance
MAXLIK implements the Cholesky factorization, solve, and update methods for the BFGS, DFP, and Newton algorithms. Event Count and Duration Regression
An included COUNT module (by Gary King, Harvard University) estimates limited dependent variable models. These procedures provide maximum likelihood estimator s for parametric regression models of events data, i.e., models with dependent variables that are measured either as event counts or as durations between events.
Platform: Windows, Mac and Linux.
Requirements: GAUSS/GAUSS Light version 8.0 or higher.