Maximum Likelihood Estimation with Stata

Sribney, William, Gould, William, Pitblado, Jeffrey

ISBN 10: 1597180122 / ISBN 13: 9781597180122

Published by Taylor & Francis Group, 2005

Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. Seller Inventory # 56619121-6

Synopsis:

Written by the creators of Stata's likelihood maximization features, Maximum Likelihood Estimation with Stata, Third Edition continues the pioneering work of the previous editions. Emphasizing practical implications for applied work, the first chapter provides an overview of maximum likelihood estimation theory and numerical optimization methods. With step-by-step instructions, the next several chapters detail the use of Stata to maximize user-written likelihood functions. Various examples include logit, probit, linear, Weibull, and random-effects linear regression as well as the Cox proportional hazards model. The final chapters describe how to add a new estimation command to Stata. Assuming a familiarity with Stata, this reference is ideal for researchers who need to maximize their own likelihood functions.

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)

Product Description: Emphasizing practical implications for applied work, this title provides an overview of maximum likelihood estimation theory and numerical optimization methods. It details the use of Stata to maximize user-written likelihood functions. It is useful for researchers who need to maximize their own likelihood functions.

Bibliographic Details

Title: Maximum Likelihood Estimation with Stata
Publisher: Taylor & Francis Group
Publication Date: 2005
Binding: Soft cover
Condition: Very Good
Edition: 3rd Edition