Abstract
Two of the most commonly used approaches to the statistical estimation of parameters are the method of maximum likelihood and the method of least squares. This chapter begins by reviewing the principle of each of these methods and some properties of the estimators. Then the method of maximum likelihood is used for generalized linear models. Usually the estimates have to be obtained numerically by an iterative procedure which turns out to be closely related to weighted least squares estimation.
Keywords
Generalize Linear Model Maximum Likelihood Estimate Maximum Likelihood Estimator Successive Approximation Information Matrix
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Copyright information
© Annette J. Dobson 1983