Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute
maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata,
but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command
to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on
the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood
evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects
linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about
the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.