|
3
|
Random Effects Modeling of Categorical Response Data
– Alan Agresti, James G. Booth, James P. Hobert, Brian Caffo
- 2000
|
|
2
|
Ascent-based Monte Carlo EM
– Brian S. Caffo, Wolfgang Jank, Galin L. Jones
- 2004
|
|
5
|
Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM
– Wolfgang Jank
- 2005
|
|
|
1.1 Mixed Models and Boosting........................ 1
– Florian Reithinger, Florian Reithinger, Florian Reithinger
|
|
1
|
Automatic Generalized Nonparametric Regression via Maximum Likelihood
– James Hobert, M. P. Wand
- 2000
|
|
2
|
MONTE CARLO LIKELIHOOD INFERENCE FOR MISSING DATA MODELS
– Ju Sung, Charles J. Geyer
|
|
2
|
Negative Binomial Loglinear Mixed Models
– James G. Booth, George Casella, Herwig Friedl, James P. Hobert
|
|
24
|
Geoadditive Models
– E. E. Kammann, M. P. Wand
- 2000
|
|
|
A Contribution to the Encyclopedia of Environmetrics
– Markov Chain Monte Carlo, Galin L. Jones, James P. Hobert
- 2000
|
|
|
Have Turnout Eflects Really Declined? Testing the Partisan Implications of Marginal Voters
– Michael D. Martinez, Jeff Gill
- 2002
|
|
|
unknown title
– O. Capp, E. Moulines, C. P. Robert
|
|
1
|
On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models
– By Douc, R. Douc, E. Moulines, C. P. Robert
|
|
|
Local Influence for Generalized Linear Mixed Models
– Hong-Tu Zhu, Sik-Yum Lee
- 2003
|
|
3
|
Simple Incorporation of Interactions Into Additive Models
– Brent A. Coull, David RUPPERT, M. P. Wand
- 2000
|
|
36
|
A tutorial on MM algorithms
– David R. Hunter, Kenneth Lange, Departments Of Biomathematics, Human Genetics
- 2004
|
|
|
Purpose of workshop Data example: cherries
– Rasmus Waagepetersen
|
|
|
A computational framework for empirical Bayes inference
– Yves F. Atchadé
|
|
5
|
Markov Chain Monte Carlo Methods in Biostatistics
– Andrew Gelman, Donald B. Rubin
- 1996
|
|
3
|
Monte Carlo EM With Importance Reweighting and Its Applications in Random Effects Models
– Fernando A. Quintana , Jun S. Liu , Guido E. del Pino
- 1999
|
