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Multi-parameter regression survival modeling: An alternative to proportional hazards

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  • K. Burke
  • G. MacKenzie

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  • K. Burke & G. MacKenzie, 2017. "Multi-parameter regression survival modeling: An alternative to proportional hazards," Biometrics, The International Biometric Society, vol. 73(2), pages 678-686, June.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:2:p:678-686
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    File URL: http://hdl.handle.net/10.1111/biom.12625
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    References listed on IDEAS

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    1. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
    2. Lu Tian & David Zucker & L.J. Wei, 2005. "On the Cox Model With Time-Varying Regression Coefficients," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 172-183, March.
    3. Jeffrey J. Gaynor, 1987. "The Use of Time Dependent Covariates in Modelling Data from an Occupational Cohort Study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 340-351, November.
    4. Jianxin Pan, 2003. "On modelling mean-covariance structures in longitudinal studies," Biometrika, Biometrika Trust, vol. 90(1), pages 239-244, March.
    5. Torben Martinussen & Thomas H. Scheike & Ib M. Skovgaard, 2002. "Efficient Estimation of Fixed and Time‐varying Covariate Effects in Multiplicative Intensity Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 57-74, March.
    6. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    7. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    8. Angela Noufaily & M. C. Jones, 2013. "Parametric quantile regression based on the generalized gamma distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(5), pages 723-740, November.
    9. D. Zeng & D. Y. Lin, 2007. "Maximum likelihood estimation in semiparametric regression models with censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 507-564, September.
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    Cited by:

    1. Chrys Caroni, 2022. "Regression Models for Lifetime Data: An Overview," Stats, MDPI, vol. 5(4), pages 1-11, December.
    2. Worku Biyadgie Ewnetu & Irène Gijbels & Anneleen Verhasselt, 2024. "Two-piece distribution based semi-parametric quantile regression for right censored data," Statistical Papers, Springer, vol. 65(5), pages 2775-2810, July.
    3. Parfait Munezero, 2022. "Efficient particle smoothing for Bayesian inference in dynamic survival models," Computational Statistics, Springer, vol. 37(2), pages 975-994, April.
    4. Kevin Burke & Frank Eriksson & C. B. Pipper, 2020. "Semiparametric multiparameter regression survival modeling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 555-571, June.

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