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Semiparametric multiparameter regression survival modeling

Author

Listed:
  • Kevin Burke
  • Frank Eriksson
  • C. B. Pipper

Abstract

We consider a log‐linear model for survival data, where both the location and scale parameters depend on covariates, and the baseline hazard function is completely unspecified. This model provides the flexibility needed to capture many interesting features of survival data at a relatively low cost in model complexity. Estimation procedures are developed, and asymptotic properties of the resulting estimators are derived using empirical process theory. Finally, a resampling procedure is developed to estimate the limiting variances of the estimators. The finite sample properties of the estimators are investigated by way of a simulation study, and a practical application to lung cancer data is illustrated.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:scjsta:v:47:y:2020:i:2:p:555-571
    DOI: 10.1111/sjos.12416
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    References listed on IDEAS

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    2. Mullen, Katharine M., 2014. "Continuous Global Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i06).
    3. Zeng, Donglin & Lin, D.Y., 2007. "Efficient Estimation for the Accelerated Failure Time Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1387-1396, December.
    4. 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.
    5. Torben Martinussen & Christian Bressen Pipper, 2014. "Estimation of Causal Odds of Concordance using the Aalen Additive Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 141-151, March.
    6. 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. Adam Braima S. Mastor & Abdulaziz S. Alghamdi & Oscar Ngesa & Joseph Mung’atu & Christophe Chesneau & Ahmed Z. Afify, 2023. "The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data," Mathematics, MDPI, vol. 11(2), pages 1-26, January.

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