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Cargando... Maximum Likelihood Estimation with Stata, Fourth Edition (1999 original; edición 2010)por William Gould, Jeffrey Pitblado, Brian Poi
Información de la obraMaximum Likelihood Estimation with Stata, Third Edition por William Gould (1999)
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Maximum Likelihood Estimation with Stata, Fourth Editionis 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. your own estimation commands. No se han encontrado descripciones de biblioteca. |
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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) ( )