**Fri. 1-2, Rm. MTH 2400
Fall '09**

** Organizational Meeting was held Wed., Sept. 9, 1pm,
MTH 2400**

** All Future Meetings will be held Fridays 1-2pm,
MTH 2400**

**Eric
Slud, Paul
Smith**,
Statistics Program ,
Math Department

Interested participants should get in touch with
either of us at **evs@math.umd.edu** or
**pjs@math.umd.edu**

**RIT Focus:** to understand and work with Statistical
Computing tools for (Frequentist and Bayesian) data analysis using
mixed effect and multilevel and hierarchical linear and
generalized-linear models. We will read papers and textbook chapters
to understand the interplay between fixed-effect models, random-effect
models, and mixed-effect models with multiple random-effect
levels. Issues of model identifiability, consistent estimation and
random-effect prediction underlie the use of multilevel-model
statistical software, and lead naturally to a discussion of
hierarchical models and Bayesian tools, including the Gibbs Sampler
and MCMC/Bugs. The computational problems arising in likelihood
calculations for multilevel models are known to be formidable, and we
will discuss various frequentist and Bayesian computational
approaches, implemented primarily in R and SAS.

**Prerequisites:** Participants should have had some
upper-level course in Mathematical Statistics (at the level of Stat
420 or higher) and some introduction to Statistical Computing (at the
level of Stat 430 for SAS or Stat 705 for R, but other Stat computing
languages would also be OK). Some familiarity with linear or GLM
models would be helpful.

**Topics by Keyword:
**

estimation, prediction of random effects, goodness of fit

methods and diagnostics for convergence

Laplace Method, penalized quasi-likelihood, adaptive Gaussian quadrature,

posterior predictive checks, simulated posterior quantities

**Reading List**
(Still under construction)

**Online Talks &
Slides**

To see a series of two "Minicourse" lectures I gave several
years ago (in 2004) on

Markov Chain Monte Carlo, click for Lecture 1 and Lecture 2 .

**Books**

Gelman, A. and Hill, J. (2007) **Data Analysis using Regression and
Multilevel/Hierarchical Models**, Cambridge.

Hartung, J., Knapp, G., and Sinha, Bimal (2008), **Statistical
Meta-Analysis with Applications**, New York: Wiley.

McCulloch, C. and Searle, S. (2001) **Generalized, Linear
and Mixed Models**, Wiley.

Meng, X., Shao, Q.-M., and Ibrahim, J. (2001), **Monte-Carlo
Methods in Bayesian Computation**, Springer-Verlag.

Pinheiro, J. and Bates, D. (2000), **Mixed-Effects Models in
S and S-PLUS**, Springer-Verlag.

** Miscellaneous Papers & Reports**

Breslow, N. and Clayton, D. (1993), Approximate Inference in
Generalized Linear Mixed Models, * Jour. of Amer. Statist. Assoc.*

Casella, G. and George, E. (1992), Explaining the Gibbs Sampler,
*Amer. Statistician* **46**, 167-173.

Ghosh M., and Rao J.N.K. (1994), Small Area Estimation: An
Appraisal, *Statistical
Science*, **9**, 55-93.

Jiang, Jiming (1999), Conditional inference about generalized
linear mixed models. *Ann. Statist.* **27**, 1974-2007.

Moura and Holt (1999), Small Area Estimation Using Multilevel
Models, *Survey Methodology *, **25**, 73-80.

Slud, E. (2000), Accurate Calculation and Maximization of
Log-Likelihood for Mixed Logistic Regression, Census
SAIPE Tech Rep.

**Organizational meeting:**Wed., Sept. 9**General Intro to Mixed & Multilevel Models: Min Tang**Fri., Sept. 18**Small Area Estimation via mixed-effect empirical-Bayes models: Vlad Beresovsky**Fri., Sept. 25**Theoretical background introducing MCMC and Gibbs Sampler: Dave Shaw**, Fri., Oct. 2**Computational tools in R and WinBugs for Bayesian estimation: Neung Soo Ha**, Fri., Oct. 9**Something about R and WinBugs in Multilevel Models: Jin Yan**, Fri., Oct. 16**Douglas Galagate on Bayesian Chi-Square Goodness of Fit Test**, Fri., Oct. 23.

on V. Johnson (2004) Ann. Statist. article**Huitian Emmy Lei on: Meta-analysis (Hartung, Knapp and Sinha 2008 book)**, Fri., Oct. 30.

in relation to multi-level models**Tentatively Jiraphan Suntornchost on: Frequentist computations in multilevel models,**, Fri., Nov. 6

specifically Breslow-Clayton (1993) paper on Penalized Quasi-likelihood**Wei Guo, topic related to Monte Carlo EM methods**, postponed to Fri., Nov. 20.**Paul Smith, Convergence Diagnostics for Markov Chain Monte Carlo**, Fri., Dec. 4.

© Last updated November 13, 2009.