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Identification of a competing risks model with unknown transformations of latent failure times

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  • Sokbae Lee

Abstract

This paper is concerned with identification of a competing risks model with unknown transformations of latent failure times. The model includes, as special cases, competing risks versions of proportional hazards, mixed proportional hazards and accelerated failure time models. It is shown that covariate effects on latent failure times, cause-specific link functions and the joint survivor function of the disturbance terms can be identified without relying on modelling the dependence between latent failure times parametrically nor using an exclusion restriction among covariates. As a result, the paper provides an identification result about the joint survivor function of the latent failure times conditional on covariates. Copyright 2006, Oxford University Press.

Suggested Citation

  • Sokbae Lee, 2006. "Identification of a competing risks model with unknown transformations of latent failure times," Biometrika, Biometrika Trust, vol. 93(4), pages 996-1002, December.
  • Handle: RePEc:oup:biomet:v:93:y:2006:i:4:p:996-1002
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    File URL: http://hdl.handle.net/10.1093/biomet/93.4.996
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    References listed on IDEAS

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    1. Bo E. Honoré & Adriana Lleras-Muney, 2006. "Bounds in Competing Risks Models and the War on Cancer," Econometrica, Econometric Society, vol. 74(6), pages 1675-1698, November.
    2. Gorgens, Tue & Horowitz, Joel L., 1999. "Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable," Journal of Econometrics, Elsevier, pages 155-191.
    3. Jaap H. Abbring & Gerard J. van den Berg, 2003. "The identifiability of the mixed proportional hazards competing risks model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 701-710.
    4. Horowitz, Joel L. & Lee, Sokbae, 2004. "Semiparametric estimation of a panel data proportional hazards model with fixed effects," Journal of Econometrics, Elsevier, vol. 119(1), pages 155-198, March.
    5. Fermanian, Jean-David, 2003. "Nonparametric estimation of competing risks models with covariates," Journal of Multivariate Analysis, Elsevier, vol. 85(1), pages 156-191, April.
    6. Joel L. Horowitz, 1999. "Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 67(5), pages 1001-1028, September.
    7. Horowitz, Joel L, 2001. "Nonparametric Estimation of a Generalized Additive Model with an Unknown Link Function," Econometrica, Econometric Society, vol. 69(2), pages 499-513, March.
    8. Horowitz, Joel L, 1996. "Semiparametric Estimation of a Regression Model with an Unknown Transformation of the Dependent Variable," Econometrica, Econometric Society, vol. 64(1), pages 103-137, January.
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    Cited by:

    1. Ruixuan Liu, 2016. "A Competing Risks Model with Time-varying Heterogeneity and Simultaneous Failure," Emory Economics 1603, Department of Economics, Emory University (Atlanta).
    2. Horny, Guillaume & Picchio, Matteo, 2010. "Identification of lagged duration dependence in multiple-spell competing risks models," Economics Letters, Elsevier, vol. 106(3), pages 241-243, March.
    3. Effraimidis, Georgios, 2016. "Nonparametric Identification of a Time-Varying Frailty Model," COHERE Working Paper 2016:6, University of Southern Denmark, COHERE - Centre of Health Economics Research.
    4. D’Haultfœuille, Xavier & Maurel, Arnaud, 2013. "Inference on an extended Roy model, with an application to schooling decisions in France," Journal of Econometrics, Elsevier, vol. 174(2), pages 95-106.
    5. Christian N. Brinch, 2011. "Non‐parametric identification of the mixed proportional hazards model with interval‐censored durations," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 343-350, July.
    6. Patrick Bayer & Shakeeb Khan & Christopher Timmins, 2008. "Nonparametric Identification and Estimation in a Generalized Roy Model," NBER Working Papers 13949, National Bureau of Economic Research, Inc.

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