- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Journal of Probability and Statistics
Volume 2012 (2012), Article ID 617678, 26 pages
Bayesian Approach to Zero-Inflated Bivariate Ordered Probit Regression Model, with an Application to Tobacco Use
1Department of Economics, Andrew Young School of Policy Studies, Georgia State University, P.O. Box 3992, Atlanta, GA 30302, USA
2Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612, USA
Received 13 July 2011; Revised 18 September 2011; Accepted 2 October 2011
Academic Editor: Wenbin Lu
Copyright © 2012 Shiferaw Gurmu and Getachew A. Dagne. 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.
- C. Calhoun, “Estimating the distribution of desired family size and excess fertility,” The Journal of Human Resources, vol. 24, pp. 709–724, 1989.
- C. Calhoun, “Desired and excess fertility in Europe and the United States: indirect estimates from World Fertility Survey data,” European Journal of Population, vol. 7, no. 1, pp. 29–57, 1991.
- A. A. Weiss, “A bivariate ordered probit model with truncation: helmet use and motor cycle injuries,” Applied Statistics, vol. 42, pp. 487–499, 1993.
- J. S. Butler and P. Chatterjee, “Tests of the specification of univariate and bivariate ordered probit,” The Review of Economics and Statistics, vol. 79, pp. 343–347, 1997.
- A. Biswas and K. Das, “A bayesian analysis of bivariate ordinal data. Wisconsin epidemiologic study of diabetic retinopathy revisited,” Statistics in Medicine, vol. 21, no. 4, pp. 549–559, 2002.
- Z. Sajaia, “Maximum likelihood estimation of a bivariate ordered probit model: implementation and Monte Carlo simulations,” Tech. Rep., The World Bank, Working Paper, 2008.
- M. K. Munkin and P. K. Trivedi, “Bayesian analysis of the ordered probit model with endogenous selection,” Journal of Econometrics, vol. 143, no. 2, pp. 334–348, 2008.
- P. E. Stephan, S. Gurmu, A. J. Sumell, and G. C. Black, “Who's patenting in the university? Evidence from the Survey of Doctorate Recipients,” Economics of Innovation and New Technology, vol. 16, pp. 71–99, 2007.
- D. Lambert, “Zero-inflated poisson regression, with an application to defects in manufacturing,” Technometrics, vol. 31, no. 1, pp. 1–14, 1992.
- S. Gurmu and P. K. Trivedi, “Excess zeros in count models for recreational trips,” Journal of Business and Economic Statistics, vol. 14, no. 4, pp. 469–477, 1996.
- J. Mullahy, “Heterogeneity, excess zeros, and the structure of count data models,” Journal of Applied Econometrics, vol. 12, no. 3, pp. 337–350, 1997.
- S. Gurmu and J. Elder, “A bivariate zero-inflated count data regression model with unrestricted correlation,” Economics Letters, vol. 100, pp. 245–248, 2008.
- D. B. Hall, “Zero-inflated Poisson and binomial regression with random effects: a case study,” Biometrics, vol. 56, no. 4, pp. 1030–1039, 2000.
- G. A. Dagne, “Hierarchical Bayesian analysis of correlated zero-inflated count data,” Biometrical Journal, vol. 46, no. 6, pp. 653–663, 2004.
- M. N. Harris and X. Zhao, “A zero-inflated ordered probit model, with an application to modelling tobacco consumption,” Journal of Econometrics, vol. 141, no. 2, pp. 1073–1099, 2007.
- D. B. Hall and J. Shen, “Robust estimation for zero-inflated Poisson regression,” Scandinavian Journal of Statistics, vol. 37, no. 2, pp. 237–252, 2010.
- L. Liu, R. L. Strawderman, M. E. Cowen, and T. S. Ya-C. T. Shih, “A flexible two-part random effects model for correlated medical costs,” Journal of Health Economics, vol. 29, no. 1, pp. 110–123, 2010.
- S. Chib and B. H. Hamilton, “Bayesian analysis of cross-section and clustered data treatment models,” Journal of Econometrics, vol. 97, no. 1, pp. 25–50, 2000.
- D. Gamerman, Markov Chain Monte Carlo, Chapman & Hall, London, UK, 1997.
- A. Gelman, J. Carlin, H. Stern, and D. B. Rubin, Bayesian Data Analysis, Chapman & Hall, London, UK, 1995.
- W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Markov Chain Monte Carlo in Practice, Chapman & Hall, London, UK, 1996.
- L. Tierney, “Markov chains for exploring posterior distributions (with discussion),” The Annals of Statistics, vol. 22, no. 4, pp. 1701–1762, 1994.
- C. J. Geyer, “Practical markov chain Monte Carlo (with discussion),” Statistical Science, vol. 7, pp. 473–511, 1992.
- A. E. Raftery and S. Lewis, “Comment: one long run with diagnostics: implementation strategies for Markov Chain Monte Carlo,” Statistical Science, vol. 7, pp. 493–549, 1992.
- D. J. Spiegelhalter, N. G. Best, B. P. Carlin, and A. van der Linde, “Bayesian measures of model complexity and fit (with discussion),” Journal of the Royal Statistical Society: Series B, vol. 64, no. 4, pp. 583–639, 2002.
- S. Gurmu and M. Yunus, “Tobacco chewing, smoking and health knowledge: evidence from Bangladesh,” Economics Bulletin, vol. 9, no. 12, pp. 1–9, 2008.