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Original Articles

A class of bivariate regression models for discrete and/or continuous responses

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Pages 2359-2383 | Received 02 May 2017, Accepted 11 Feb 2018, Published online: 08 May 2018

References

  • Al-Mutairi, D., M. Ghitany, and D. Kundu. 2011. A new bivariate distribution with weighted exponential marginals and its multivariate generalization. Statistical Papers 52:921–36.
  • Alzaid, A. A., F. E. Almuhayfith, and M. A. Omair. 2016. Bivariate regression models based on compound poisson distribution. Communications in Statistics-Theory and Methods pp. 1–15.
  • Atkinson, A. C. 1985. Plots, Transformations, and Regression. Oxford: Oxford University Press.
  • Balakrishnan, N., and C. D. Lai. 2009. Continuous Bivariate Distributions, 2nd ed. Springer: Dordrecht.
  • Beilei, W. 2013. Contributions to copula modeling of mixed discrete-continuous outcomes. PhD thesis, University of Calgary.
  • Berkhout, P., and E. Plug. 2004. A bivariate Poisson count data model using conditional probabilities. Statistica Neerlandica 58:349–64.
  • Catalano, P. J., and L. M. Ryan. 1992. Bivariate latent variable models for clustered discrete and continuous outcomes. Journal of the American Statistical Association 87:651–8.
  • Cook, R. D. 1977. Detection of influential observation in linear regression. Technometrics 19:15–18.
  • Cook, R. D. 1986. Assessment of local influence (with discussion). Journal of the Royal Statistical Society Series B 48:133–69.
  • Core, Team R. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Cox, D. R. 1972. The analysis of multivariate binary data. Journal of the Royal Statistical Society Series C 21:113–20.
  • De Leon, A. R., and B. Wu. 2011. Copula-based regression models for a bivariate mixed discrete and continuous outcome. Statistics in Medicine 30:175–85.
  • Dunn, P. K., and G. K. Smyth. 1996. Randomized quantile residuals. Journal of Computational and Graphical Statistics 5:236–44.
  • Fitzmaurice, G. M., and N. M. Laird. 1995. Regression models for a bivariate discrete and continuous outcome with clustering. Journal of the American Statistical Association 90:845–52.
  • Fitzmaurice, G. M., and N. M. Laird. 1997. Regression models for mixed discrete and continuous responses with potentially missing values. Biometrics 53:110–22.
  • George, E. O., D. Armstrong, P. J. Catalano, and D. K. Srivastava. 2007. Regression models for analyzing clustered binary and continuous outcomes under an assumption of exchangeability. Journal of Statistical Planning and Inference 137:3462–74.
  • Gomes, E. M. d. C. 2007. Análise de sensibilidade e resíduos em modelos de regressão com respostas bivariadas por meio de cópulas. PhD thesis, Universidade de São Paulo.
  • Gueorguieva, R. 2001. A multivariate generalized linear mixed model for joint modelling of clustered outcomes in the exponential family. Statistical Modelling 1:177–93.
  • Gueorguieva, R. V., and A. Agresti. 2001. A correlated probit model for joint modeling of clustered binary and continuous responses. Journal of the American Statistical Association 96:1102–12.
  • Gupta, A. K., J. M. Orozco-Castañeda, and D. K. Nagar. 2011. Non-central bivariate beta distribution. Statistical Papers 52:139–52.
  • Hanagal, D. D. 2006. Bivariate weibull regression model based on censored samples. Statistical Papers 47:137–47.
  • Khan, S., B. Pratikno, A. I. Ibrahim, and R. M. Yunus. 2016. The correlated bivariate noncentral f distribution and its application. Communications in Statistics-Simulation and Computation 45:3491–507.
  • Kim, Y. J. 2016. Cure rate model with bivariate interval censored data. Communications in Statistics-Simulation and Computation.
  • McCullagh, P., and J. A. Nelder. 1989. Generalized Linear Models. London: Chapman & Hall.
  • McDonald, B. W. 1993. Estimating logistic regression parameters for bivariate binary data. Journal of the Royal Statistical Society Series B 55:391–7.
  • Paula, G. A. 2004. Modelos de Regressão com Apoio Computacional. São Paulo: IME-USP.
  • Samani, E. B., and M. Ganjali. 2014. Mixed correlated bivariate ordinal and negative binomial longitudinal responses with nonignorable missing values. Communications in Statistics-Theory and Methods 43:2659–73.
  • Scollnik, D. P. 2002. Regression models for bivariate loss data. North American Actuarial Journal 6:67–80.
  • Shults, J., W. Sun, X. Tu, H. Kim, J. Amsterdam, J. M. Hilbe, and T. Ten-Have. 2009. A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data. Statistics in Medicine 28:2338–55.
  • Song, J., H. X. Barnhart, and R. H. Lyles. 2004. A GEE approach for estimating correlation coefficients involving left-censored variables. Journal of Data Science 2:245–57.
  • Thomas, W., and R. D. Cook. 1989. Assessing influence on regression coefficients in generalized linear models. Biometrika 76:741–9.
  • Verbeke, G., and G. Molenberghs. 2000. Linear Mixed Models for Longitudinal Data. New York: Springer-Verlag.
  • Yang, Y., and J. Kang. 2010. Joint analysis of mixed poisson and continuous longitudinal data with nonignorable missing values. Computational Statistics & Data Analysis 54:193–207.
  • Yang, Y., J. Kang, K. Mao, and J. Zhang. 2007. Regression models for mixed poisson and continuous longitudinal data. Statistics in Medicine 26:3782–800.

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