Constrained Maximum Likelihood

Authors:
Ronald Schoenberg
Affiliations:
Aptech Systems, Inc. and The University of Washington, USA
Venue:
Computational Economics - Special issue on computational economics in Geneva: volume 1: computational econometrics, statistics, and optimization
Year:
1997

Citing 4
Cited 0

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Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

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Abstract

Constrained Maximum Likelihood (CML), developed at AptechSystems, generates maximum likelihood estimates with generalparametric constraints (linear or nonlinear, equality orinequality), using the sequential quadratic programming method.CML computes two classes of confidence intervals,by inversion of the Wald and likelihood ratio statistics, and by simulation. The inversion techniques can producemisleading test sizes, but Monte Carlo evidence suggests thisproblem can be corrected under certain circumstances.