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Robust estimators of accelerated failure time regression with generalized log-gamma errors

Author

Listed:
  • Agostinelli, Claudio
  • Locatelli, Isabella
  • Marazzi, Alfio
  • Yohai, Víctor J.

Abstract

The generalized log-gamma (GLG) model is a very flexible family of distributions to analyze datasets in many different areas of science and technology. Estimators are proposed which are simultaneously highly robust and highly efficient for the parameters of a GLG distribution in the presence of censoring. Estimators with the same properties for accelerated failure time models with censored observations and error distribution belonging to the GLG family are also introduced. It is proven that the proposed estimators are asymptotically fully efficient and the maximum mean square error is examined using Monte Carlo simulations. The simulations confirm that the proposed estimators are highly robust and highly efficient for a finite sample size. Finally, the benefits of the proposed estimators in applications are illustrated with the help of two real datasets.

Suggested Citation

  • Agostinelli, Claudio & Locatelli, Isabella & Marazzi, Alfio & Yohai, Víctor J., 2017. "Robust estimators of accelerated failure time regression with generalized log-gamma errors," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 92-106.
    • Handle: RePEc:eee:csdana:v:107:y:2017:i:c:p:92-106
    DOI: 10.1016/j.csda.2016.10.012
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    References listed on IDEAS

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    1. Agostinelli, Claudio & Marazzi, Alfio & Yohai, Víctor J. & Randriamiharisoa, Alex, 2016. "Robust Estimation of the Generalized Loggamma Model: The R Package robustloggamma," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i07).
    2. Jianguo Sun & Qiming Liao & Marcello Pagano, 1999. "Regression Analysis of Doubly Censored Failure Time Data with Applications to AIDS Studies," Biometrics, The International Biometric Society, vol. 55(3), pages 909-914, September.
    3. Ortega, Edwin M. M. & Bolfarine, Heleno & Paula, Gilberto A., 2003. "Influence diagnostics in generalized log-gamma regression models," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 165-186, February.
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