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Mixed Model-Based Hazard Estimation

Author

Listed:
  • Cai, T.
  • Hyndman, R.J.
  • Wand, M.P.

Abstract

We propose a new method for estimation of the hazard function from a set of censored failure time data, with a view to extending the general approach to more complicated models. The approach is based on a mixed model representation of penalized spline hazard estimators. One payoff is the automation of the smoothing parameter choice through restricted maximum likelihood. Another is the option to use standard mixed model software for automatic hazard estimation.

Suggested Citation

  • Cai, T. & Hyndman, R.J. & Wand, M.P., 2000. "Mixed Model-Based Hazard Estimation," Monash Econometrics and Business Statistics Working Papers 11/00, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2000-11
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2000/wp11-00.pdf
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    References listed on IDEAS

    as
    1. Bruce Bloxom, 1985. "A constrained spline estimator of a hazard function," Psychometrika, Springer;The Psychometric Society, vol. 50(3), pages 301-321, September.
    2. Rebecca A. Betensky & Jane C. Lindsey & Louise M. Ryan & M. P. Wand, 1999. "Local EM Estimation of the Hazard Function for Interval-Censored Data," Biometrics, The International Biometric Society, vol. 55(1), pages 238-245, March.
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    Citations

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    Cited by:

    1. Kauermann, Goran & Khomski, Pavel, 2006. "Additive two-way hazards model with varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1944-1956, December.
    2. Kauermann, Goran, 2005. "Penalized spline smoothing in multivariable survival models with varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 169-186, April.
    3. Kneib, Thomas, 2006. "Mixed model-based inference in geoadditive hazard regression for interval-censored survival times," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 777-792, November.
    4. Kauermann, Goran & Xu, Ronghui & Vaida, Florin, 2008. "Stacked Laplace-EM algorithm for duration models with time-varying and random effects," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2514-2528, January.

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    More about this item

    Keywords

    Non-parametric regression; Restricted maximum likelihood; Variance component; Survival analysis.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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