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Improved likelihood inference in Birnbaum-Saunders regressions

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  • Lemonte, Artur J.
  • Ferrari, Silvia L.P.
  • Cribari-Neto, Francisco

Abstract

The Birnbaum-Saunders regression model is commonly used in reliability studies. We address the issue of performing inference in this class of models when the number of observations is small. Our simulation results suggest that the likelihood ratio test tends to be liberal when the sample size is small. We obtain a correction factor which reduces the size distortion of the test. Also, we consider a parametric bootstrap scheme to obtain improved critical values and improved p-values for the likelihood ratio test. The numerical results show that the modified tests are more reliable in finite samples than the usual likelihood ratio test. We also present an empirical application.

Suggested Citation

  • Lemonte, Artur J. & Ferrari, Silvia L.P. & Cribari-Neto, Francisco, 2010. "Improved likelihood inference in Birnbaum-Saunders regressions," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1307-1316, May.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:5:p:1307-1316
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    References listed on IDEAS

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    1. 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.
    2. Lemonte, Artur J. & Cribari-Neto, Francisco & Vasconcellos, Klaus L.P., 2007. "Improved statistical inference for the two-parameter Birnbaum-Saunders distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4656-4681, May.
    3. Xie, Feng-Chang & Wei, Bo-Cheng, 2007. "Diagnostics analysis for log-Birnbaum-Saunders regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4692-4706, May.
    4. Francisco Cribari-Neto & Spyros Zarkos, 2003. "Econometric and Statistical Computing Using Ox," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 277-295, June.
    5. James Carpenter, 1998. "Assessing parameter uncertainty via bootstrap likelihood ratio confidence regions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(5), pages 639-649, June.
    6. Manuel Galea & Victor Leiva-Sanchez & Gilberto Paula, 2004. "Influence Diagnostics in log-Birnbaum-Saunders Regression Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(9), pages 1049-1064.
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    Cited by:

    1. Francisco M. C. Medeiros & Silvia L. P. Ferrari, 2017. "Small-sample testing inference in symmetric and log-symmetric linear regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(3), pages 200-224, August.
    2. Lemonte, Artur J., 2013. "A new extended Birnbaum–Saunders regression model for lifetime modeling," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 34-50.
    3. Vargas, Tiago M. & Ferrari, Silvia L.P. & Lemonte, Artur J., 2014. "Improved likelihood inference in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 110-124.
    4. 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.
    5. Lemonte, Artur J. & Ferrari, Silvia L.P., 2011. "Size and power properties of some tests in the Birnbaum-Saunders regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1109-1117, February.
    6. Li, Ai-Ping & Xie, Feng-Chang, 2012. "Diagnostics for a class of survival regression models with heavy-tailed errors," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4204-4214.

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