Maximum Likelihood Estimation with Stata, Third Edition

William Gould; Jeffrey Pitblado; William Sribney

ISBN 10: 1597180122 / ISBN 13: 9781597180122

Published by Stata Press, 2006

About this Item

Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.43.

Seller Inventory # G1597180122I3N00

Bibliographic Details

• Title: Maximum Likelihood Estimation with Stata, Third Edition
• Publisher: Stata Press
• Publication Date: 2006
• Language: English
• ISBN 10: 1597180122
• ISBN 13: 9781597180122
• Binding: Paperback
• Edition: 3rd Edition
• Condition: Good
• Dust Jacket Condition: No Jacket

About this title

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)