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In This Volume The Underlying Logic And Practice Of Maximum Likelihood (Ml) Estimation Is Made Clear By Providing A General Modeling Framework That Utilizes The Tools Of Ml Methods. This Framework Offers Readers A Flexible Modeling Strategy Since It Accommodates Cases From The Simplest Linear Models To The Most Complex Nonlinear Models That Link A System Of Endogenous And Exogenous Variables With Nonnormal Distributions. Using Examples To Illustrate The Techniques Of Finding Ml Estimators And Estimates, Eliason Discusses: What Properties Are Desirable In An Estimator; Basic Techniques For Finding Ml Solutions; The General Form Of The Covariance Matrix For Ml Estimates; The Sampling Distribution Of Ml Estimators; The Application Of Ml In The Normal Distribution As Well As In Other Useful Distributions; And Some Helpful Illustrations Of Likelihoods.
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