000			01636nam a2200157Ia 4500
008			140223b1993 xxu||||| |||| 00| 0 eng d
020			_a9780803941076 _c0.00
082			_a001.42-96 _bELI
100			_aElisason, Scott R.
245			_aMaximum Likelihood Estimation: Logic and Practice
260			_aNew Delhi _bSage Publications India Pvt. Ltd. _c1993
300			_a85p
500			_aCONTENTS 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
890			_aIndia
995			_AELI _D611.00 _E0 _F049 _G3293 _H0 _I0.00 _UC _W19990415 _XMobel Book Distributors _ZGeneral
999			_c71048 _d71048