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A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models
W. Oberhofer
J. Kmenta
Abstract
This paper describes an iterative procedure for obtaining maximum likelihood estimates of the parameters of a generalized regression model when direct maximization with respect to all parameters is difficult. A proof of convergence and some interesting applications are provided.
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