Maximum Likelihood Estimation: Logic and Practice

CONTENTS Series Editor's Introduction V 1. Introduction: The Logic of Maximum Likelihood Background and Preliminaries The Principle of Maximum Likelihood Desirable Properties of Estimators 2. A General Modeling Framework Using Maximum Likelihood Methods The Normal PDF Model Simple Z Tests and Confidence Intervals: The Homoscedastic Normal PDF Model Likelihood Ratio Tests: The Heteroscedastic Normal PDF Model Wald Tests A General Measure of Association for ML Models 3. An Introduction to Basic Estimation Techniques The Score Vector, Hessian Matrix and Sampling Distribution of the MLE The Iterative Process and Updating Methods Obtaining Start Values The Update Step and Checking for the Solution 4. Further Empirical Examples The Gamma PDF Model Constant Coefficient of Variation Model Sources of Variability in CV The Multinomial PF Model The Bivariate Normal PDF Model 5. Additional Likelihoods The Truncated Normal PDF Model The Log-Normal PDF Model 6. Conclusions Appendix: Gauss Code for Some of the Models in the Monograph Notes References About the Author