Maximum
Likelihood
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
Improved Algorithm
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, Linux and Solaris.
Requirements: GAUSS/GAUSS Light version 8.0 or higher.
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