Maximum likelihood estimation with stata Gould, William

By: Gould, William
Contributor(s): Pitblado, Jeffrey | Poi, Brian
Publisher: College Station, Texas A Stata Press Publication 2010Edition: 4th edDescription: xxii, 352 p.ISBN: 9781597180788Subject(s): Computer and information system | Stata | Social sciences - Statistical methods - Computer programs | Statistische Analyse | Maximum-Likelihood-SchätzungDDC classification: 005.55 Summary: 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.
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Books Vikram Sarabhai Library
Slot 82 (0 Floor, West Wing) Non-fiction 005.55 G6M2 (Browse shelf) Available 177921

Includes bibliographical references (p. [343]-345) and indexes

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.

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