Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 2801081, 5 pages
http://dx.doi.org/10.1155/2016/2801081
Research Article

A Bayesian Approach for Evaluation of Determinants of Health System Efficiency Using Stochastic Frontier Analysis and Beta Regression

Department of Statistics, Ondokuz Mayıs University, 55139 Samsun, Turkey

Received 25 January 2016; Accepted 15 March 2016

Academic Editor: Chuangyin Dang

Copyright © 2016 Talat Şenel and Mehmet Ali Cengiz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. B. Evans, A. Tandon, C. J. L. Murray, and J. A. Lauer, “The comparative efficiency of national health systems in producing health: an analysis of 191 countries,” GPE Discussion Paper Series no. 29, World Health Organization, Geneva, Switzerland, 2000. View at Google Scholar
  2. A. Tandon, C. J. L. Murray, J. A. Lauer, and D. B. Evans, “Measuring overall health system performance for 191 countries,” GPE Discussion Paper Series 30, World Health Organization, Geneva, Switzerland, 2001. View at Google Scholar
  3. B. Hollingsworth and J. Wildman, “The efficiency of health production: re-estimating the WHO panel data using parametric and non-parametric approaches to provide additional information,” Health Economics, vol. 12, no. 6, pp. 493–504, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Greene, “Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization's panel data on national health care systems,” Health Economics, vol. 13, no. 10, pp. 959–980, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. J. van den Broeck, G. Koop, J. Osiewalski, and M. F. J. Steel, “Stochastic frontier models: a Bayesian perspective,” Journal of Econometrics, vol. 61, no. 2, pp. 273–303, 1994. View at Publisher · View at Google Scholar · View at Scopus
  6. G. Koop, M. F. J. Steel, and J. Osiewalski, “Posterior analysis of stochastic frontier models using Gibbs sampling,” Computational Statistics, vol. 10, pp. 353–373, 1995. View at Google Scholar
  7. Y. Kim and P. Schmidt, “A review and empirical comparison of bayesian and classical approaches to inference on efficiency levels in stochastic frontier models with panel data,” Journal of Productivity Analysis, vol. 14, no. 2, pp. 91–118, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. L. A. Kurkalova and A. Carriquiry, “An analysis of grain production decline during the early transition in Ukraine: a Bayesian inference,” American Journal of Agricultural Economics, vol. 84, no. 5, pp. 1256–1263, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. K. C. Ennsfellner, D. Lewis, and R. I. Anderson, “Production efficiency in the Austrian insurance industry: a Bayesian examination,” Journal of Risk and Insurance, vol. 71, no. 1, pp. 135–159, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. J. E. Griffin and M. F. J. Steel, “Bayesian stochastic frontier analysis using WinBUGS,” Journal of Productivity Analysis, vol. 27, no. 3, pp. 163–176, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. E. G. Tsionas and E. N. Papadakis, “A Bayesian approach to statistical inference in stochastic DEA,” Omega, vol. 38, no. 5, pp. 309–314, 2010. View at Publisher · View at Google Scholar
  12. B. M. Tabak and P. Langsch Tecles, “Estimating a Bayesian stochastic frontier for the Indian banking system,” International Journal of Production Economics, vol. 125, no. 1, pp. 96–110, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Wranik, “Healthcare policy tools as determinants of health-system efficiency: evidence from the OECD,” Health Economics, Policy and Law, vol. 7, no. 2, pp. 197–226, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. P. H. de Cos and E. Moral-Benito, “Determinants of health-system efficiency: evidence from OECD countries,” International Journal of Health Care Finance and Economics, vol. 14, no. 1, pp. 69–93, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. V. Paris, M. Devaux, and L. Wei, “Health systems institutional characteristics: a survey of 29 OECD countries,” OECD Health Working Papers no. 50, OECD Publishing, 2010. View at Google Scholar
  16. S. L. P. Ferrari and F. Cribari-Neto, “Beta regression for modelling rates and proportions,” Journal of Applied Statistics, vol. 31, no. 7, pp. 799–815, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. J. Buckley, “Estimation of models with beta-distributed dependent variables: a replication and extension of Paolino's study,” Political Analysis, vol. 11, no. 2, pp. 204–205, 2003. View at Publisher · View at Google Scholar
  18. A. J. Branscum, W. O. Johnson, and M. C. Thurmond, “Bayesian beta regression: applications to household expenditure data and genetic distance between foot-and-mouth disease viruses,” Australian & New Zealand Journal of Statistics, vol. 49, no. 3, pp. 287–301, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Cepeda-Cuervo and L. Garrido, “Bayesian beta regression models with joint mean and dispersion modeling,” Monte Carlo Methods and Applications, vol. 21, no. 1, pp. 49–58, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. E. Cepeda-Cuervo, D. Jaimes, M. Marín, and J. Rojas, “Bayesian beta regression with Bayesianbetareg R-package,” Computational Statistics, vol. 31, no. 1, pp. 165–187, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Aigner, C. A. Lovell, and P. Schmidt, “Formulation and estimation of stochastic frontier production function models,” Journal of Econometrics, vol. 6, no. 1, pp. 21–37, 1977. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  22. P. L. Espinheira, S. L. P. Ferrari, and F. Cribari-Neto, “On beta regression residuals,” Journal of Applied Statistics, vol. 35, no. 4, pp. 407–419, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. W. Zhao, R. Zhang, Y. Lv, and J. Liu, “Variable selection for varying dispersion beta regression model,” Journal of Applied Statistics, vol. 41, no. 1, pp. 95–108, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. Z. Or, J. Wang, and D. Jamison, “International differences in the impact of doctors on health: a multilevel analysis of OECD countries,” Journal of Health Economics, vol. 24, no. 3, pp. 531–560, 2005. View at Publisher · View at Google Scholar · View at Scopus