Prediction of finite population parameters using parametric model under some loss functions

References

• Berger, J. O. 1984. The robust Bayesian viewpoint (with discussion). In Robustness of Bayesian analysis, ed. J. Kadane, 63–124. Amsterdam: North-Holland.
   Google Scholar
• Betro, B., and F. Ruggeri. 1992. Conditional Γ-minimax actions under convex Losses. Communications in Statistics - Theory and Methods 21 (4):1051–66. doi:https://doi.org/10.1080/03610929208830830.
   Web of Science ®Google Scholar
• Bolfarine, H. 1990. Bayesian linear prediction in finite populations. Annals of the Institute of Statistical Mathematics 42 (3):435–44. doi:https://doi.org/10.1007/BF00049300.
   Web of Science ®Google Scholar
• Boratynska, A. 2002. Posterior regret Γ-minimax estimation in normal model with asymmetric loss function. Applicationes Mathematicae 29:7–13.
   Google Scholar
• Boratynska, A. 2006. Robust Bayesian prediction with asymmetric loss function In poisson model of insurance risk. Acta Universitatis Lodziensis. Folia Oeconomica 196:123–38.
   Google Scholar
• Chen, Q., M. R. Elliott, and R. J. A. Little. 2012. Bayesian inference for finite population quantiles from unequal probability samples. Survey Methodology 38 (2):203–14.
   PubMedGoogle Scholar
• Ericson, W. A. 1969. Subjective Bayesian models in sampling finite populations (with discussion). Journal of the Royal Statistical Society: Series B (Methodological) 31 (2):195–233. doi:https://doi.org/10.1111/j.2517-6161.1969.tb00782.x.
   Google Scholar
• Ghosh, M. 2008. Robust estimation in finite population sampling. Institute of Mathematical Statistics 1:116–22.
   Google Scholar
• Ghosh, M., and D. H. Kim. 1993. Robust Bayes estimation of the finite population mean. The Indian Journal of Statistics 55:322–42.
   Google Scholar
• Godambe, V. P. 1955. A unified theory of sampling from finite populations. The Journal of the Royal Statistical Society, Ser. B 17:268–78.
   Google Scholar
• Godambe, V. P., and V. M. Joshi. 1965. Admissibility and Bayes estimation in sampling finite populations I. The Annals of Mathematical Statistics 36 (6):1707–22. doi:https://doi.org/10.1214/aoms/1177699799.
   Google Scholar
• Godambe, V. P., and M. E. Thompson. 1971. The specification of prior knowldege by classes of prior distribution in survey sampling estimation. In Foundations of statistical inference, ed. V. P. Godambe and D. A. Sprott, 243–58. Toronto: Holt, Rinehart and Winston.
   Google Scholar
• Hamner, M. S., W. Seaman, and M. Young. 2001. Bayesian methods in finite population sampling. Proceedings of the Annual Meeting of the American Statistical Association. http://ww2.amstat.org/sections/SRMS/Proceedings/y2001/Proceed/00059.pdf
   Google Scholar
• Hill, B. 1968. Posterior distribution of percentiles: Bayes theorem for sampling from a population. Journal of the American Statistical Association 63 (322):677–91. doi:https://doi.org/10.2307/2284038.
   Web of Science ®Google Scholar
• Jafaraghaie, R., and N. Nematollahi. 2016. Admissibility of linear predictors of finite population parameters under reflected normal loss function. Journal of Sciences, Islamic Republic of Iran 27:4.
   Google Scholar
• Jafaraghaie, R., and N. Nematollahi. 2018. Robust Bayesian prediction under a general linear-exponential posterior risk function and its application in finite population. Communications in Statistics: Theory and Methods 47 (13):3269–92. doi:https://doi.org/10.1080/03610926.2017.1383425.
   Google Scholar
• Jafari Jozani, M., and A. Parsian. 2008. Posterior regret Γ-minimax estimation and prediction based on k-record data under entropy loss function. Communications in Statistics - Theory and Methods 37 (14):2202–12. doi:https://doi.org/10.1080/03610920701855038.
   Google Scholar
• Kaminska, A., and Z. Porosinski. 2009. On robust Bayesian estimation under some asymmetric and bounded loss function. Statistics 43:253–65.
   Web of Science ®Google Scholar
• Kiapour, A., and N. Nematollahi. 2011. Robust Bayesian prediction and estimation under a squared log error loss function. Statistics & Probability Letters 81 (11):1717–24. doi:https://doi.org/10.1016/j.spl.2011.07.002.
   Web of Science ®Google Scholar
• Kim, H. M., and A. M. E. Saleh. 2008. Prediction of finite population total with measurement error models. In Recent advances in linear models and related areas, 79–93. Heidelberg: Springer, Physica-Verlag.
   Google Scholar
• Little, R. J. A. 2004. To model or not to model? Competing modes of inference for finite population sampling. Journal of the American Statistical Association 99 (466):546–56. doi:https://doi.org/10.1198/016214504000000467.
   Web of Science ®Google Scholar
• Liu, X. Q., and J. Y. Rong. 2007. Quadratic prediction problems in finite populations. Statistics & Probability Letters 77 (5):483–9. doi:https://doi.org/10.1016/j.spl.2006.08.015.
   Web of Science ®Google Scholar
• Meczarski, M., and R. Zielinski. 1991. Stability of the Bayesian estimator of the Poisson mean under the inexactly specified gamma prior. Statistics and Probability Letters 12:329–33.
   Web of Science ®Google Scholar
• Pefeffermann, D., and C. R. Rao. 2009. Handbook of Statistics, vol. 6. Amsterdam: Elsevier Science.
   Google Scholar
• Rios Insua, D., F. Ruggeri, and B. Vidakovic. 1995. Some results on posterior regret Γ-minimax estimation. Statistics and Decisions 13:315–31.
   Google Scholar
• Si, Y., N. Pillai, and A. Gelman. 2015. Bayesian nonparametric weighted sampling inference. Bayesian Analysis 10 (3):605–21. doi:https://doi.org/10.1214/14-BA924.
   Web of Science ®Google Scholar
• Yih, Y. (Ed.). 2016. Handbook of healthcare delivery systems. Boca Raton, FL: CRC Press.
   Google Scholar
• Zangeneh, S. Z., and R. J. A. Little. 2015. Bayesian inference for the finite population total from a heteroscedastic probability proportional to size sample. Journal of Survey Statistics and Methodology 3 (2):162–92. doi:https://doi.org/10.1093/jssam/smv002.
   Google Scholar