|
Akaike, H. (1974). A new look at the statistical model identification. Automatic Control, IEEE Transactions on, 19(6), 716-723.
Anderson, T. W. (1957). Maximum likelihood estimates for a multivariate normal distribution when some observations are missing. Journal of the american Statistical Association, 52(278), 200-203.
Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. In 2nd Int. Symp. on Information Theory (Edited by B. N. Petrov and F. Csaki), 267 – 281. Akademiai Kiado, Budapest.
Barndorff‐Nielsen, O. E., & Shephard, N. (2001). Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 167-241.
Basilevsky, A. T. (2009). Statistical factor analysis and related methods: theory and applications (Vol. 418). John Wiley & Sons.
Bohning, D., Dietz, E., Schaub, R., Schlattmann, P., & Lindsay, B. G. (1994).The distribution of the likelihood ratio for mixtures of densities from the oneparameter exponential family. Annals of the Institute of Statistical Mathematics, 46(2), 373-388.
Cook, R. D., & Johnson, M. E. (1986). Generalized Burr-Pareto-logistic distributions with applications to a uranium exploration data set. Technometrics, 28(2), 123-131.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological), 1-38.
Efron, B., & Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical science, 54-75.
Fokoué, E., & Titterington, D. M. (2003). Mixtures of factor analysers. Bayesian estimation and inference by stochastic simulation. Machine Learning, 50(1-2), 73-94.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109.
Healy, M. (1968). Multivariate normal plotting. Applied Statistics, 157-161.
Hocking, R. R., & Smith, W. B. (1968). Estimation of parameters in the multivariate normal distribution with missing observations. Journal of the American Statistical Association, 63(321), 159-173.
Jamshidian, M. (1997). An EM algorithm for ML factor analysis with missing data. In Latent variable modeling and applications to causality (pp. 247-258). Springer New York.
Jöreskog, K. G., & Sörbom, D. (1975). Statistical models and methods for analysis of longitudinal data. University of Uppsala, Department of Statistics.
Kim, J. O., & Curry, J. (1977). The treatment of missing data in multivariate analysis. Sociological Methods & Research, 6(2), 215-240.
Lawley, D. N., & Maxwell, A. E. (1971). Factor analysis as a statistical method.
Lin, T. I., & Lin, T. C. (2011). Robust statistical modelling using the multivariate skew t distribution with complete and incomplete data. Statistical Modelling, 11(3), 253-277.
Lin, T. I., Wu, P. H., McLachlan, G. J., & Lee, S. X. (2014). A robust factor analysis model using the restricted skew-t distribution. Test, 1-22.
Lindsay, B. G. (1995, January). Mixture models: theory, geometry and applications. In NSF-CBMS regional conference series in probability and statistics (pp. i-163). Institute of Mathematical Statistics and the American Statistical Association.
Liu, C. (1999). Efficient ML estimation of the multivariate normal distribution from incomplete data. Journal of Multivariate Analysis, 69(2), 206-217.
Liu, M., & Lin, T. I. (2015). Skew-normal factor analysis models with incomplete data. Journal of Applied Statistics, 42(4), 789-805.
Meng, X. L., & Rubin, D. B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika, 80(2), 267-278.
Murray, P. M., Browne, R. P., & McNicholas, P. D. (2014a). Mixtures of skew-t factor analyzers. computational Statistics & Data Analysis, 77, 326- 335.
Murray, P. M., McNicholas, P. D., & Browne, R. P. (2014b). A mixture of common skew‐t factor analysers. Stat, 3(1), 68-82.
Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T. I., Maier, L. M., ... & Mesirov, J. P. (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences, 106(21), 8519-8524.
Rubin, D. B. (1987) Multiple Imputation for Nonresponse in Surveys. Wiley.
Sahu, S. K., Dey, D. K., & Branco, M. D. (2003). A new class of multivariate skew distributions with applications to Bayesian regression models. Canadian Journal of Statistics, 31(2), 129-150.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461-464.
Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American statistical Association,82(398), 528-540.
|