Maximum Likelihood Estimation with Stata - Couverture souple

Gould, William

 
9781597180122: Maximum Likelihood Estimation with Stata
Couverture souple
ISBN 10 : 1597180122 ISBN 13 : 9781597180122
Editeur : Stata Press, 2005

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)

    • Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

    Présentation de l'éditeur

    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)

    • Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

    Éditeur
    Stata Press
    Date d'édition
    2005
    Langue
    anglais
    ISBN 10 
    1597180122
    ISBN 13 
    9781597180122
    Reliure
    Broché
    Numéro d'édition
    3
    Nombre de pages
    290
    Coordonnées du fabricant
    non disponible
    Personne responsable
    non disponible