RAND Statistics Group Staff Bios

A-B-C-D

Lane Burgette

Bayesian statistics; multinomial probit models; selection and switching models; latent factor quantile regression; imputation techniques for missing data; quantile regression; and data confidentiality.

Q Burkhart

Data analysis, sampling, health applications

Siddhartha Dalal

Health care markets, civil laws, regulation, and insurance

E-F-G-H

Marc Elliott

Sampling; Categorical Data Analysis; Case-Mix Adjustment; Propensity Score Technique; Experimental Design; Survey Mode Effects

Lionel Galway

Data analysis, statistical computing, risk analysis

Bonnie Ghosh-Dastidar

Applied statistics, Multiple imputation, Non-response, Multilevel models, Survey design and analysis

Daniela Golinelli

Bayesian Statistics, Hierarchical Models, Propensity Scores, MCMC, Hidden Markov Models

Beth Ann Griffin

Survival analysis; biostatistics; causal modeling; design of clinical and non-clinical studies

Ann Haas

Data analysis, education and health applications

Katrin Hambarsoomian

Data Imputation, Weighting, Longitudinal Analysis, Case-Mix Adjustment

Bing Han

Bayesian statistics; simultaneous inference; linear model; multivariate analysis; longitudinal data analysis; categorical data analysis; computation; semiparametric regression

Amelia Haviland (adjunct)

Analysis of observational data, propensity score analysis, latent trajectory group mixture modeling, nonparametrics, sampling, bootstrapping under complex sampling

I-J-K-L

J.R. Lockwood

Value-added modeling, hierarchical modeling, Bayesian methods

M-N-O-P

Lou Mariano

Cross-classified models; Latent variable models; Bayesian hierarchical models; Markov Chain Monte Carlo techniques; statistical applications to mental measurement; Bayesian model selection; survey sampling, with emphasis on non-ignorable missing data

Daniel McCaffrey

Value-Added Modeling, Analysis of clustered data, Propensity score methods for causal modeling

Brett Munjas

Data analysis, biostatistics, applied regression, sampling, statistical programming

Susan Paddock

Statistics, Bayesian methods, missing data, hierarchical models, Bayesian nonparametrics

Q-R-S-T

Daniel Relles (adjunct)

Statistical Computing, Data Analysis, Sampling, Linear Models, Data Management, Military Logistics and Health Applications

Greg Ridgeway

Modeling massive datasets and data mining, non-parametric function estimation for prediction, boosting and optimization, propensity score analysis of observational

Terrance Dean Savitsky

Bayesian Non-parametric models: Focus on high dimensional variable selection under non-linearity and presence of covariate and observation clustering

Lara Schmidt

Navigation and timing, space systems, risk analysis, time series analysis

Claude Setodji

Non-parametric modeling, Clustering and analysis of observational data, Sufficient (variable) dimension reduction and its applications

Mary Ellen Slaughter

Applied regression, categorical data analysis, survival analysis, epidemiology, statistical programming, cost-effectiveness, health care databases, geographic information systems

Daniel Sommerhauser

Forecasting, model selection, statistical programming, cluster analysis, non-parametric modeling

Marika Suttorp

Meta-analysis, Statistical programming, Statistics in health, GIS