DOI QR코드

DOI QR Code

Korean Welfare Panel Data: A Computational Bayesian Method for Ordered Probit Random Effects Models

  • Lee, Hyejin (Department of Statistics, Duksung Women's University) ;
  • Kyung, Minjung (Department of Statistics, Duksung Women's University)
  • Received : 2013.10.24
  • Accepted : 2013.12.12
  • Published : 2014.01.31

Abstract

We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

Keywords

References

  1. Albert, J. H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data, Journal of the American Statistical Association, 88, 669-679. https://doi.org/10.1080/01621459.1993.10476321
  2. Andrew, D. Martin, Quinn, K. M. and Park, J. H. (2011). MCMCpack: Markov Chain Monte Carlo in R, Journal of Statistical Software, 42, 1-21.
  3. Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models, Journal of the American Statistical Association, 88, 9-25.
  4. Buonaccorsi, J. P. (1996). Measurement error in the response in the general linear model, Journal of the American Statistical Association, 91, 633-642. https://doi.org/10.1080/01621459.1996.10476932
  5. Chib, S., Greenberg, E. and Chen, Y. (1998). MCMC methods for fitting and comparing multinomial response models, Technical Report, Economics Working Paper Archive, Washington University at St. Louis.
  6. Chib, S. and Winkelmann, R. (2001). Markov Chain Monte Carlo Analysis of Correlated Count data, Journal of Business and Economic Statistics, 19, 428-435. https://doi.org/10.1198/07350010152596673
  7. Damien, P., Wakefield, J. and Walker, S. (1999). Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables, Journal of the Royal Statistical Society, Series B, 61, 331-344. https://doi.org/10.1111/1467-9868.00179
  8. Dey, D. K., Ghosh, S. K. and Mallick, B. K. (2000). Generalized Linear Models: A Bayesian Perspective, Marcel Dekker, New York.
  9. Fahrmeir, L. and Tutz, G. (2001). Multivariate Statistical Modelling Based on Generalized Linear Models, Second Edition, Springer, New York.
  10. Fehir, J. S. (1988). Self-rated health status, self-efficacy, motivation, and selected demographics as determinants of health-promoting lifestyle behavior in men 35 to 64 years old: A nursing investigation, Doctoral Dissertation, The University of Texas at Austin.
  11. Fillenbaum, G. G. (1979). Social context and self-assessments of health among the elderly, Journal of Health and Social Behavior, 20, 45-51. https://doi.org/10.2307/2136478
  12. Gill, J. and Casella, G. (2009). Nonparametric priors for ordinal Bayesian social science models:Specification and estimation, Journal of the American Statistical Association, 104, 453-454. https://doi.org/10.1198/jasa.2009.0039
  13. Hoeymans, N., Feskens, E. J., Kromhout, D. and van den Bos, G. A. (1997). Ageing and the relation-ship between functional status and self-rated health in elderly man, Social science and Medicine, 45, 1527-1536. https://doi.org/10.1016/S0277-9536(97)00089-0
  14. Jiang, J. (2007). Linear and Generalized Linear Mixed Models and Their Applications, Springer-Verlag, New York.
  15. Lee, M. and Kim, D. (2013). Predictors of Korean Elderly People's self-rated Health Status and Mod-erating Effects of Socio-Economic Position. The Korean Journal of Community Living Science, 24, 37-49. https://doi.org/10.7856/kjcls.2013.24.1.37
  16. Luoh, M. and Herzog, A. R. (2002). Individual consequences of volunteer and paid work in old age:Health and mortality, Journal of Health and Social Behavior, 43, 490-509. https://doi.org/10.2307/3090239
  17. McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, Second Edition, Chapman & Hall, New York.
  18. McCulloch, C. E. and Searle, S. R. (2001). Generalized, Linear, and Mixed Models, John Wiley & Sons, New York.
  19. Neal, R. M. (2003). Slice sampling, Annals of Statistics, 31, 705-741. https://doi.org/10.1214/aos/1056562461
  20. Oh, Y. H., Bae, H. O. and Kim, Y. S. (2006). A study on physical and mental function affecting self-perceived health of older persons in Korea. Journal of the Korean Gerontological Society, 26, 461-476.
  21. Otiniano, M. E., Cu, X. L., Ottenbacher, K. and Markides, K. S. (2003). The effect of diabetes combined with stroke on disability, self-rated health, and mortality in older Mexican Americans, Academy of Physical Medicines and Rehabilitation, 84, 725-730.
  22. Scott, W. K., Macera, C. A., Cornman, C. B. and Sharpe, P. A. (1997). Functional health status as a predictor of mortality in men and women over 65. Journal of Clinical Epidemiology, 50, 291-296. https://doi.org/10.1016/S0895-4356(96)00365-4
  23. Stoller, E. P. (1984). Self-Assessments of health by the elderly: The impact of informal assistance, Journal of Health and Social Behavior, 25, 260-270. https://doi.org/10.2307/2136424
  24. von dem Knesebeck, O., Luschen, G., Cocherham,W. C. and Siegrist, J. (2003). Socioeconomic status and health among the aged in the United States and Germany: A comparative cross-sectional study, Social Science and Medicine, 57, 1643-1652. https://doi.org/10.1016/S0277-9536(03)00020-0
  25. Wang, N., Lin, X., Gutierrez, R. G. and Carroll, R. J. (1998). Bias analysis and SIMEX approach in generalized linear mixed measurement error models, Journal of the American Statistical Association, 93, 249-261. https://doi.org/10.1080/01621459.1998.10474106
  26. Ware, J. E. Jr. (1987). Standards for validating health measures: Definition and content, Journal of Chronic Diseases, 40, 473-480. https://doi.org/10.1016/0021-9681(87)90003-8
  27. Wolfinger, R. and O'Connell, M. (1993). Generalized linear mixed models: A Pseudo-likelihood approach, Journal of Statistical Computation and Simulation, 48, 233-243. https://doi.org/10.1080/00949659308811554