IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v107y2017icp92-106.html

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947316302390
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2016.10.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amarnath Nandy & Abhik Ghosh & Ayanendranath Basu & Leandro Pardo, 2025. "Robust Hypothesis Testing and Model Selection for Parametric Proportional Hazards Regression Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(2), pages 454-525, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carlos A. Cardozo & Carlos E. Alonso-Malaver, 2025. "Semiparametric Model-Assisted Approach to Probabilistic Sampling of Finite Populations With High Right-Skew and Kurtosis," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(1), pages 29-58, May.
    2. Yu, Binbing, 2010. "A Bayesian MCMC approach to survival analysis with doubly-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1921-1929, August.
    3. Aldo M. Garay & Victor H. Lachos & Heleno Bolfarine & Celso R. B. Cabral, 2017. "Linear censored regression models with scale mixtures of normal distributions," Statistical Papers, Springer, vol. 58(1), pages 247-278, March.
    4. Guillermo Martínez-Flórez & David Elal-Olivero & Carlos Barrera-Causil, 2021. "Extended Generalized Sinh-Normal Distribution," Mathematics, MDPI, vol. 9(21), pages 1-24, November.
    5. Artur J. Lemonte & Alexandre G. Patriota, 2011. "Influence diagnostics in Birnbaum--Saunders nonlinear regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 871-884, February.
    6. Guillermo Martínez-Flórez & Rafael B. Azevedo-Farias & Roger Tovar-Falón, 2022. "New Class of Unit-Power-Skew-Normal Distribution and Its Associated Regression Model for Bounded Responses," Mathematics, MDPI, vol. 10(17), pages 1-24, August.
    7. Ortega, Edwin M.M. & Cordeiro, Gauss M. & Lemonte, Artur J., 2012. "A log-linear regression model for the β-Birnbaum–Saunders distribution with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 698-718.
    8. Yang-Jin Kim, 2006. "Regression Analysis of Doubly Censored Failure Time Data with Frailty," Biometrics, The International Biometric Society, vol. 62(2), pages 458-464, June.
    9. Roger Tovar-Falón & Guillermo Martínez-Flórez & Heleno Bolfarine, 2022. "Modelling Asymmetric Data by Using the Log-Gamma-Normal Regression Model," Mathematics, MDPI, vol. 10(7), pages 1-16, April.
    10. Hashimoto, Elizabeth M. & Ortega, Edwin M.M. & Paula, Gilberto A. & Barreto, Mauricio L., 2011. "Regression models for grouped survival data: Estimation and sensitivity analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 993-1007, February.
    11. Carrasco, Jalmar M.F. & Ortega, Edwin M.M. & Paula, Gilberto A., 2008. "Log-modified Weibull regression models with censored data: Sensitivity and residual analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 4021-4039, April.
    12. Lucia Santana & Filidor Vilca & V�ctor Leiva, 2011. "Influence analysis in skew-Birnbaum--Saunders regression models and applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1633-1649, July.
    13. Shuwei Li & Jianguo Sun & Tian Tian & Xia Cui, 2020. "Semiparametric regression analysis of doubly censored failure time data from cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 315-338, April.
    14. Silva, Giovana Oliveira & Ortega, Edwin M.M. & Cancho, Vicente G. & Barreto, Mauricio Lima, 2008. "Log-Burr XII regression models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3820-3842, March.
    15. Guillermo Martínez-Flórez & Inmaculada Barranco-Chamorro & Héctor W. Gómez, 2021. "Flexible Log-Linear Birnbaum–Saunders Model," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
    16. Leiva, Victor & Barros, Michelli & Paula, Gilberto A. & Galea, Manuel, 2007. "Influence diagnostics in log-Birnbaum-Saunders regression models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5694-5707, August.
    17. Fabio, Lizandra C. & Paula, Gilberto A. & Castro, Mário de, 2012. "A Poisson mixed model with nonnormal random effect distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1499-1510.
    18. Yessenia A. Gil & Aldo M. Garay & Victor H. Lachos, 2025. "Likelihood-based inference for interval censored regression models under heavy-tailed distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 34(3), pages 519-544, July.
    19. Carlos A. Dos Santos & Daniele C. T. Granzotto & Vera L. D. Tomazella & Francisco Louzada, 2018. "Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis," JRFM, MDPI, vol. 11(1), pages 1-12, March.
    20. Edwin Ortega & Fernanda Rizzato & Clarice Demétrio, 2009. "The generalized log-gamma mixture model with covariates: local influence and residual analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(3), pages 305-331, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:107:y:2017:i:c:p:92-106. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.