Bibliografía: p. 343-345.

1. Theory and practice -- 2. Introduction to ml -- 3. Overview of ml -- 4. Method lf -- 5. Methods lf0, lf1 and lf2 -- 6. Methods d0, d1 and d2 -- 7. Debugging likelihood evaluators -- 8. Setting initial values -- 9. Interactive maximization -- 10. Final results -- 11. Mata-based likelihood evaluators -- 12. Writing do-files to maximize likelihoods -- 13. Writing ado-files to maximize likelihoods -- 14. Writing ado-files for survey data analysis -- 15. Other examples -- A: syntax of ml -- B: likelihood-evaluator checklists -- C: listing of estimation commands.

Written by the creators of Stata's likelihood maximization features, "Maximum Likelihood Estimation with Stata, Third Edition" continues the pioneering work of the previous editions. Emphasizing practical implications for applied work, the first chapter provides an overview of maximum likelihood estimation theory and numerical optimization methods. With step-by-step instructions, the next several chapters detail the use of Stata to maximize user-written likelihood functions. Various examples include logit, probit, linear, Weibull, and random-effects linear regression as well as the Cox proportional hazards model. The final chapters describe how to add a new estimation command to Stata. Assuming a familiarity with Stata, this reference is ideal for researchers who need to maximize their own likelihood functions. New ml commands and their functions: Constraint: fits a model with linear constraints on the coefficient by defining your constraints; accepts a constraint matrix ml model: picks up survey characteristics; accepts the subpop option for analyzing survey data optimization algorithms: Berndt-Hall-Hall-Hausman (BHHH), Davidon-Fletcher-Powell (DFP), Broyden-Fletcher-Goldfarb-Shanno (BFGS) ml: switches between optimization algorithms; computes variance estimates using the outer product of gradients (OPG).