References
- Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Csaki, F., eds. Second International Symposium of Information Entropy Theory. Budapest: Akademia Kiado, pp. 267–281.
- Box, G. E.P., Jenkins, G.M., Reinsel, G. (1994). Time Series Analysis: Forecasting and Control. 3rd ed. Englewood Cliffs: Prentice Hall.
- Brockwell, P.J., Davis, R.A. (1991). Time Series: Theory and Methods. Springer Series in Statistics. 2nd ed.
- Cavanaugh, J.E., Neath, A.A. (1999). Generalizing the derivation of the Schwarz information criterion. Communications in Statistics Theory and Methods 28: 49–66.
- Cavanaugh, J.E., Shumway, R.H. (1998). An Akaike information criterion for model selection in the presence of incomplete data. Journal of Statistical Planning and Inference 67: 45–65.
- Chen, C., Davis, A.R., Brockwell, P.J., Bai, Z. (1993). Order determination for autoregressive processes using resampling methods. Statistica Sinica 3: 481–500.
- Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from incomplete data via EM algorithm. Journal of the Royal Statistical Society: Series B 39(1): 1–38.
- Ibrahim, J.G., Zhu, H., Tang, N. (2008). Model selection criteria for missing-data problems using the EM algorithm. Journal of the American Statistical Association 103(484): 1648–1658.
- Little, R. J.A., Rubin, D.B. (1987). Statistical Analysis with Missing Data. New York: Wiley.
- McLachlan, G., Peel, D. (2000). Finite Mixture Models. New York: Wiley.
- McLachlan, G.J., Krishnan, T. (1997). The EM Algorithm and Extensions. New York: Wiley-Interscience.
- Meng, X.L., Rubin, D.B. (1991). Using EM algorithm to obtain asymptotic variance-covariance matrices: the SEM algorithm. Journal of the American Statistical Association 86: 899–909.
- Rao, C.R., Wu, Y. (2001). On Model Selection. IMS Lecture Notes Monograph Series. Vol. 38.
- R Development Core Team, (2011). R: A Language and Environment for Statistical Computing. Vienna Austria: R Foundation for Statistical Computing. http://www.R-project.org.
- Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics 6: 461–464.
- Shibata, R. (1976). Selection of the order of an autoregressive model by Akaike information criterion. Biometrika 63: 117–126.
- Shimodaira, H. (1994). A new criterion for selecting models from partially observed data. In: Selecting Models from Data: Artificial Intelligence and Statistics IV (Lecture Notes in Statistics). Vol. 89. New York: Springer, pp. 21–29.