"Maximum Likelihood Estimation with Stata, Fifth 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 describe how to add a new estimation command to Stata and how to write estimators using Mata, Stata's matrix programming language. The book begins with an introduction to the theory of maximum ...
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"Maximum Likelihood Estimation with Stata, Fifth 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 describe how to add a new estimation command to Stata and how to write estimators using Mata, Stata's matrix programming language. The book begins with an introduction to the theory of maximum likelihood estimation, emphasizing practical implications for applied work. An introductory chapter shows how to implement simple estimators using the Stata command mlexp, which requires no programming at all. Subsequent chapters then describe the the Stata command ml and provide details of each of the four types of likelihood estimation evaluator programs ml supports. Numerous examples are included in each chapter, including linear regression with normal errors and logit and probit regressions. Later chapters illustrate how to implement estimators like Weibull regression, random-effects linear regression, the Cox proportional hazards model, and a bivariate Poisson model. Final chapters and appendixes provide additional details about the ml command and Mata function moptimize(), provide checklists to follow when writing evaluators, and show how to write your own estimation commands that behave just like official Stata commands"--
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