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Fitting Parametric Survival Models with Time‐Dependent Covariates

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  • Trond Petersen

Abstract

This paper shows (a) how the Gauss‐Newton method for nonlinear least squares estimation can be used to estimate any fully parameteric survival model by the method of maximum likelihood; (b) how the algorithm allows for a flexible treatment of time‐dependent covariates in fully parametric models. An empirical analysis of the duration of jobs illustrates the use of the algorithm. The data are taken from The Norwegian Life History Study for Men.

Suggested Citation

  • Trond Petersen, 1986. "Fitting Parametric Survival Models with Time‐Dependent Covariates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(3), pages 281-288, November.
    • Handle: RePEc:bla:jorssc:v:35:y:1986:i:3:p:281-288
    DOI: 10.2307/2348028
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    Cited by:

    1. Bent Jesper Christensen & Malene Kallestrup‐Lamb, 2012. "The Impact Of Health Changes On Labor Supply: Evidence From Merged Data On Individual Objective Medical Diagnosis Codes And Early Retirement Behavior," Health Economics, John Wiley & Sons, Ltd., vol. 21(S1), pages 56-100, June.
    2. Jason Karceski & Steven Ongena & David C. Smith, 2005. "The Impact of Bank Consolidation on Commercial Borrower Welfare," Journal of Finance, American Finance Association, vol. 60(4), pages 2043-2082, August.
    3. D. Scott Bennett, 1997. "Measuring Rivalry Termination, 1816-1992," Journal of Conflict Resolution, Peace Science Society (International), vol. 41(2), pages 227-254, April.

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