Scoring with Constraints
نویسنده
چکیده
This report considers the solution of estimation problems based on the maximum likelihood principle when a xed number of equality constraints are imposed on the problem. Consistency and the asymptotic distribution of the parameter estimates as n ! 1, where n is the number of observations, are discussed, and it is shown that a suitably scaled limiting multiplier vector is known. It is suggested that this information should permit the good properties of Fisher's method of scoring for the unconstrained case to be extended to a class of augmented Lagrangian methods for the constrained case.
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