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Maximum Likelihood Estimation: Logic and Practice Scott R. Eliason
Publisher: Sage Publications, Inc

Since being proposed by Sir Ronald Fisher in a series of papers during the period 1912 to 1934 (Aldrich, 1977), Maximum Likelihood Estimation (MLE) has been one of the "workhorses" of statistical inference, and so it plays . Thus, MLE is a method to find out parameters resulted from coefficients which maximize joint likelihood of our estimates; product of likelihoods of all n observations. As was pointed out by Gan and Jiang (1999), this logic can be reversed. As it can often be important to ensure that the likelihood function has been globally maximized, what can we do to check that this has in fact been achieved in practice? Maximum Likelihood Estimation - Logic and Practice. Journal of Business Research (forthcoming). In practice, so-called extended or modified NR algorithms have been found to. Summary - Restricted maximum likelihood estimation using first and second derivatives of the likelihood is . Maximum Likelihood Estimation: Logic and Practice, Thou - sand Oaks, California: Sage. And y'Py and their derivatives ..