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.
|Country of origin:
• Jeffrey Pitblado
• Brian Poi
||Electronic book text
||4th Revised edition
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