The one-step-late PXEM algorithm

• Albert J.H. and Chib S. 1993. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88: 669–679.

  Google Scholar 

• Dempster A.P., Laird N.M., and Rubin D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, Methodological 39: 1–37.

  Google Scholar 

• Foulley J.-L. and van Dyk D.A. 2000. The PX-EM algorithm for fast stable fitting of Henderson's mixed model. Genetics Selective Evolution 32: 143–163.

  Google Scholar 

• Gelfand A.E., Sahu S.K., and Carlin B.P. 1995. Efficient parameterization for normal linear mixed models. Biometrika 82: 479–488.

  Google Scholar 

• Goodrich R. and Caines P. 1979. Linear system identification from nonstationary cross-sectional data. IEEE Trans. Aut. Cont. AC-24: 403–411.

  Google Scholar 

• Green P.J. 1990. On use of the EM algorithm for penalized likelihood estimation. Journal of the Royal Statistical Society, Series B, Methodological 52: 443–452.

  Google Scholar 

• Gupta N. and Mehra R. 1974. Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations. IEEE Trans. Aut. Cont. AC-19: 774–783.

  Google Scholar 

• Haas M. 1994. Igg subclass deposits in glomeruli of lupus and nonlupus membranous nephropathies. American Journal of Kidney Disease 23: 358–364.

  Google Scholar 

• Haas M. 1998. Value of igg subclasses and ultrastructural markers in predicting latent membranous lupus nephritis. Modern Pathology 11: 147A.

  Google Scholar 

• Harrison P. 1967. Exponential smoothing and short-term forecasting. Man. Sci. 13: 821–842.

  Google Scholar 

• Harrison P.J. 1965. Short-term sales forecasting. Applied Statistics 14: 102–139.

  Google Scholar 

• Harvey A. 1989. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, New York.

  Google Scholar 

• Higdon D.M. 1998. Auxiliary variable methods for Markov chain Monte Carlo with applications. Journal of the American Statistical Association 93: 585–595.

  Google Scholar 

• Imai K. and van Dyk D.A. 2003. A Bayesian analysis of the multinomial probit model using marginal augmentation. Submitted to The Journal of Econometrics.

• Jazwinski A. 1970. Stochastic Processes and Filtering Theory. Academic Press, New York.

  Google Scholar 

• Kalman R. 1960. A new approach to linear filtering and prediction problems. Trans. ASME J. of Basic Eng. 8: 35–45.

  Google Scholar 

• Kalman R. and Bucy R. 1961. New results in linear filtering and prediction theory. Trans. ASME J. of Basic Eng. 83: 95–108.

  Google Scholar 

• Ledolter J. 1979. A recursive approach to parameter estimation in regression and time series models. Communications in Statistics, Part A-Theory and Methods 8: 1227–1246.

  Google Scholar 

• Little R.J. and Rubin D.B. 1987. Statistical AnalysisWith Missing Data. John Wiley & Sons, New York.

  Google Scholar 

• Liu C. and Rubin D.B. 1994. The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence. Biometrika 81: 633–648.

  Google Scholar 

• Liu C. and Rubin D.B. 1995. ML estimation of the t distribution using EM and its extensions, ECM and ECME. Statistica Sinica 5: 19–39.

  Google Scholar 

• Liu C., Rubin D.B., and Wu Y.N. 1998. Parameter expansion for EM acceleration-The PXEM algorithm. Biometrika 75: 755–770.

  Google Scholar 

• Liu J.S. and Wu Y.N. 1999. Parameter expansion scheme for data augmentation. Journal of the American Statistical Association 94: 1264–1274.

  Google Scholar 

• McCulloch R. and Rossi P. 1994. An exact likelihood analysis of the multinomial probit model. Journal of Econometrics 64: 207–240.

  Google Scholar 

• Meng X.-L. and van Dyk D.A. 1997. The EM algorithm-An old folk song sung to a fast newtune (with discussion). Journal of the Royal Statistical Society, Series B, Methodological 59: 511–567.

  Google Scholar 

• Meng X.-L. and van Dyk D.A. 1998. Fast EM implementations for mixed-effects models. Journal of the Royal Statistical Society, Series B, Methodological 60: 559–578.

  Google Scholar 

• Meng X.-L. and van Dyk D.A. 1999. Seeking efficient data augmentation schemes via conditional and marginal augmentation. Biometrika 86: 301–320.

  Google Scholar 

• Pilla R.S. and Lindsay B.G. 1999. Alternative EM methods in highdimensional finite mixtures. Biometrika submitted.

• Pole A., West M., and Harrison J. 1994. Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall, London.

  Google Scholar 

• Shumway R.H. and Stoffer D.S. 1982. An approach to time series smoothing and forecastin using the EM algorithm. Journal of Time Series Analysis 3: 253–264.

  Google Scholar 

• van Dyk D.A. 2000a. Fast new EM-type algorithms with applications in astrophysics. Technical Report.

• van Dyk D.A. 2000b. Fitting mixed-effects models using efficient EMtype algorithms. Journal of Computational and Graphical Statistics 9: 78–98.

  Google Scholar 

• van Dyk D.A. 2000c. Nesting EM algorithms for computational efficiency. Statistical Sinica 10: 203–225.

  Google Scholar 

• van Dyk D.A. and Meng X.-L. 2000. Algorithms based on data augmentation. In: Pourahmadi K. and Berk K. (Eds.), Computing Science and Statistics: Proceedings of the 31st Symposium on the Interface, 230–239. Interface Foundation of North America, Fairfax Station, VA.

  Google Scholar 

• van Dyk D.A. and Meng X.-L. 2001. The art of data augmentation. The Journal of Computational and Graphical Statistics 10: 1–111.

  Google Scholar 

• Watson W. and Engle R. 1983. Alternative algorithm for the estimation of dynamic factor, mimic and varying coefficient regression. Journal of Econometrics 23: 385–400.

  Google Scholar 

• West M. and Harrison J. 1997. Bayesian Forecasting and Dynamic Models (2nd edn.). Springer-Verlag, New York.

  Google Scholar 

• Wu C.F.J. 1983. On the convergence properties of the EM algorithms. The Annals of Statistics 11: 95–103.

  Google Scholar 

Download references