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Maximum Likelihood Estimation with Stata, Third Edition - Softcover

 
9781597180122: Maximum Likelihood Estimation with Stata, Third Edition

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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)
  • Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

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    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)
  • „Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

    • VerlagStata Press
    • Erscheinungsdatum2005
    • ISBN 10 1597180122
    • ISBN 13 9781597180122
    • EinbandTapa blanda
    • SpracheEnglisch
    • Auflage3
    • Anzahl der Seiten290

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    Sribney, William, Gould, William, Pitblado, Jeffrey
    Verlag: Taylor & Francis Group, 2005
    ISBN 10: 1597180122 ISBN 13: 9781597180122
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    Gould, William; Pitblado, Jeffrey; Sribney, William
    Verlag: Stata Press, 2006
    ISBN 10: 1597180122 ISBN 13: 9781597180122
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