Divergences based Bayesian inference with censored data

References

• Achcar, J. A., F. A. Moala, M. H. Tarumoto, and L. F. Coladello. 2015. A bivariate generalized exponential distribution derived from copula functions in the presence of censored data and covariates. Pesquisa Operacional 35 (1):165–86.
   Google Scholar
• Akritas, M. G. 1988. Pearson-type goodness-of-fit tests: The univariate case. Journal of the American Statistical Association 83 (401):222–30. doi:10.1080/01621459.1988.10478590.
   Web of Science ®Google Scholar
• Atienza, D., C. Bielza, and P. Larra∼naga. 2022. Semiparametric Bayesian networks. Information Sciences 584:564–82.
   Google Scholar
• Avlogiaris, G., A. Micheas, and K. Zografos. 2016. On testing local hypotheses via local divergence. Statistical Methodology 31:20–42. doi:10.1016/j.stamet.2016.01.003.
   Google Scholar
• Avlogiaris, G., A. C. Micheas, and K. Zografos, 2019. A criterion for local model selection. Sankhybara A: The Indian Journal of Statistics, 81, 406– 44.
   Google Scholar
• Barbieri, M. M., and J. O. Berger. 2004. Optimal predictive model selection. The Annals of Statistics 32 (3):870–97.
   Web of Science ®Google Scholar
• Batsidis, A., N. Martín, L. Pardo, and K. Zografos. 2016. ϕ-divergence based procedure for parametric change-point problems. Methodology and Computing in Applied Probability 18 (1):21–35. doi:10.1007/s11009-014-9398-3.
   Google Scholar
• Boukeloua, M. 2021. Study of semiparametric copula models via divergences with bivariate censored data. Communications in Statistics - Theory and Methods 50 (23):5429–52. doi:10.1080/03610926.2020.1734834.
   Web of Science ®Google Scholar
• Broniatowski, M., and A. Keziou. 2006. Minimization of φ-divergences on sets of signed measures. Studia Scientiarum Mathematicarum Hungarica 43 (4):403–42.
   Web of Science ®Google Scholar
• Broniatowski, M., and A. Keziou. 2009. Parametric estimation and tests through divergences and the duality technique. Journal of Multivariate Analysis 100 (1):16–36.
   Web of Science ®Google Scholar
• Cherfi, M. 2014. On Bayesian estimation via divergences. Comptes Rendus de l’Académie des Sciences - Series I - Mathematics 352:749–54.
   Google Scholar
• Chernozhukov, V., and H. Hong. 2003. An MCMC approach to classical estimation. Journal of Econometrics 115 (2):293–346.
   Web of Science ®Google Scholar
• Corcuera, J. M., and F. Giummolè. 1999. A generalized Bayes rule for prediction. Scandinavian Journal of Statistics 26 (2):265–79.
   Google Scholar
• Doğan, O., S. Taşpi̇nar, and A. K. Bera. 2021. Bayesian estimation of stochastic tail index from high-frequency financial data. Empirical Economics 61:2685–711.
   Google Scholar
• Efron, B., and I. M. Johnstone. 1990. Fisher’s information in terms of the hazard rate. The Annals of Statistics 18 (1):38– 62.
   Google Scholar
• George, E. I. 2010. Dilution priors: Compensating for model space redundancy. In Borrowing strength: Theory powering applications-A Festschrift for Lawrence D. Brown, Vol. 6, 158– 65.
   Google Scholar
• George, E. I., and R. E. McCulloch. 1993. Variable selection via gibbs sampling. Journal of the American Statistical Association 88 (423):881–9. doi:10.1080/01621459.1993.10476353.
   Web of Science ®Google Scholar
• George, E. I., and R. E. Mcculloch. 1997. Approaches for Bayesian variable selection. Statistica Sinica 7:339–73.
   Web of Science ®Google Scholar
• Kalbfleisch, J. D., and R. L. Prentice. 2002. The statistical analysis of failure time data, 2nd ed. Hoboken, NJ: John Wiley & Sons, Inc.
   Google Scholar
• Lehmann, E. L., and G. Casella. 2006. Theory of point estimation. Springer Science & Business Media.
   Google Scholar
• Lei, Y., S. Zhou, J. Filar, and N. Ye. 2023. Multi-pass bayesian estimation: A robust bayesian method. Computational Statistics 39 (4):1–34.
   Google Scholar
• Mcgilchrist, C. A., and C. W. Aisbett. 1991. Regression with frailty in survival analysis. Biometrics 142:461–6.
   Google Scholar
• Newey, W. K., and D. Mcfadden. 1994. Large sample estimation and hypothesis testing.InHandbook of econometrics, Vol. 4, 2111– 245.
   Google Scholar
• Sousa, A. R. d SN. L Garcia, and B Vidakovic. 2021. Bayesian wavelet shrinkage with beta priors. Computational Statistics 36 (2):1341–63. doi:10.1007/s00180-020-01048-1.
   Google Scholar
• Stute, W., and J.-L. Wang. 1993. The strong law under random censorship. Annals of Statistics 21 (3):1591–607.
   Google Scholar
• Zhou, M. 2015. Empirical likelihood method in survival analysis, Vol. 79. CRC Press.
   Google Scholar