Global and local tests to assess stationarity of Markov transition models

References

• Agresti, A. 2012. Categorical data analysis. 3rd ed. New York: Wiley.
   Google Scholar
• Anderson, T. W., and L. A. Goodman. 1957. Statistical inference about Markov chains. Annals of Mathematical Statistics. Ann Arbor, 28:89–110.
   Google Scholar
• Azzalini, A. 1983. Maximum likelihood estimation of order n for stationary stochastic processes. Biometrika 70:381–88.
   Google Scholar
• Bonney, G. E. 1987. Logistic Regression for dependent binary observations. Biometrics 43:951–73.
   Google Scholar
• Castro, A. C. 2016. Comportamento e desempenho sexual de suínos reprodutores em ambientes enriquecidos, PhD. dissertation. Brazil: University of São Paulo.
   Google Scholar
• Christensen, R. H. 2011. Analysis of ordinal data with cumulative link models estimation with the R-package ordinal. Available from: http://www.R-project.org.
   Google Scholar
• Cox, D. R. 1970. The analysis of binary data. London: Methuen.
   Google Scholar
• De Rooij, M. 2011. Transitional ideal point models for longitudinal multinomial outcomes. Statistical Modelling 11 (2):115–35.
   Google Scholar
• Diggle, P. J., P. J. Heagerty, K. Y. Liang, and S. L. Zeger. 2002. Analysis of longitudinal data. New York: Oxford University Press.
   Google Scholar
• Fitzmaurice, G. M., and N. M. Laird. 1993. A likelihood-based method for analysing longitudinal binary responses. Biometrika 80:141–51.
   Google Scholar
• Good, I. J. 1955. The likelihood ratio test for Markov chains. Biometrika 42:531–33.
   Google Scholar
• Goodman, L. A. 1962. Statistical methods for analysing processes of change. American Journal of Sociology 68:57–78.
   Google Scholar
• Heagerty, P. J. 2002. Marginalized transition models and likelihood inference for longitudinal data. Biometrics 85:342–51.
   Google Scholar
• Jones, P. W., and P Smith. 2001. Stochastic processes. Arnold: London.
   Google Scholar
• Koch, G. C., G. J. Carr, I. A. Amara, M. E. Stokes, and T. J. Uryniak. 1990. Categorical data analysis. In: Berry, D. A. Statistical Methodology in the Pharmaceutical Sciences. 13:389–473, New York: Marcel Dekker.
   Google Scholar
• Korn, E. L., and A. S. Whittemore. 1979. Methods for analysing panel studies of acute health effects of air pollution. Biometrics 35:715–802.
   Google Scholar
• Lee, K., and M. J. Daniels. 2007. A class of Markov models for longitudinal ordinal data. Biometrics 63:1060–67.
   Google Scholar
• Lindsey, J. K. 1995. Modelling frequency and count data. Oxford: Oxford University Press.
   Google Scholar
• Lindsey, J. K. 2004. Statistical analysis of stochastic processes in time. New York: Cambridge University Press.
   Google Scholar
• McCullagh, P. 1980. Regression Methods for Ordinal Data. Journal of The Royal Statistical Society 42:109–142.
   Google Scholar
• Molenberghs, G., and G. Verbeke. 2005. Models for discrete longitudinal data. New York: Springer-Verlag.
   Google Scholar
• Pereira, L. E. T., A. J. Paiva, E. V. Geremia, and S. C. da Silva. 2015a. Grazing management and tussock distribution in elephant grass. Grass and Forage Science 70 (2):406–17.
   Google Scholar
• Pereira, L. E. T., A. J. Paiva, E. V. Geremia, and S. C. da Silva. 2015b. Regrowth patterns of elephant grass (Pennisetum purpureum Schum) subjected to strategies of intermittent stocking management. Grass and Forage Science 70 (1):195–204.
   Google Scholar
• R. Development Core Team. 2015. R: A language and environment for statistical computing 3.2. Vienna, Austria. Available from: http://www.R-project.org.
   Google Scholar
• Ripley, B., and W. Venables. 2016. Package nnet: Feed-forward neural networks and multinomial log-linear models. Available from: http://www.R-project.org/.
   Google Scholar
• Spedicato, G. A. 2015. markovchain: discrete time Markov chains made easy. Available from: http://www.R-project.org/.
   Google Scholar
• Stirzaker, D. (2005). Stochastic Processes and Models. New York: Oxford University Press.
   Google Scholar
• Ware, J. H., S. Lipsitz, and F. E. Speizer. 1988. Issues in the Analysis of Repeated Categorical Outcomes. Statistics in Medicine 7:95–107.
   Google Scholar
• Zeger, S. L., and K. Y. Liang. 1992. An overview of methods for the analysis of longitudinal data. Statistics in Medicine II:1825–839.
   Google Scholar