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Residuals for log-Burr XII regression models in survival analysis

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  • Giovana O. Silva
  • Edwin M.M. Ortega
  • Gilberto A. Paula

Abstract

In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.

Suggested Citation

  • Giovana O. Silva & Edwin M.M. Ortega & Gilberto A. Paula, 2011. "Residuals for log-Burr XII regression models in survival analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1435-1445, June.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1435-1445
    DOI: 10.1080/02664763.2010.505950
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Beatriz R. Lanjoni & Edwin M. M. Ortega & Gauss M. Cordeiro, 2016. "Extended Burr XII Regression Models: Theory and Applications," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 203-224, March.
    2. Prataviera, Fábio & Ortega, Edwin M.M. & Cordeiro, Gauss M. & Pescim, Rodrigo R. & Verssani, Bruna A.W., 2018. "A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 13-26.

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