Research Professor, ISR; Richard D. Remington Distinguished University Professor of Biostatistics, School of Public Health; Professor of Statistics, LS&A
Little received a PhD in statistics from Imperial College, London University in the United Kingdom. His current research interests involve analysis of data with missing values; analysis of repeated measures data with drop-outs; survey sampling, focused on model-based methods for complex survey designs that are robust to misspecification and compared to the resulting inferences to classical methods based on the randomization distribution; and applications of statistics to epidemiology, public health, psychiatry, sample surveys in demography and economics, and medicine. From 2010-12 he served as the inaugural Associate Director for Research and Methodology and Chief Scientist at the U.S. Census Bureau.
Groves, R., Dillman, D., Eltinge, J. & Little, R. (2002, eds.) Survey Nonresponse. John Wiley, New York. (Winner of the 2011 American Association for Public Opinion Research Book Award).
Little, R.J.A. & Rubin, D.B. (2002). Statistical Analysis with Missing Data, 2nd Edition. New York: John Wiley.
Little, R.J.A. (2004). To Model or Not to Model? Competing Modes of Inference for Finite Population Sampling. Journal of the American Statistical Association, 99, 546-556.
Andridge, R.H. & Little, R.J. (2009). The Use of Sample Weights in Hot Deck Imputation. Journal of Official Statistics, 25, 1, 21-36.
Chen, Q., Elliott, M.R. & Little, R.J. (2010). Bayesian Penalized Spline Model-Based Estimation of the Finite Population Proportion for Probability-Proportional-to-Size Samples. Survey Methodology 36, 23-34.
Andridge, R.H. & Little, R.J. (2011). Proxy pattern-mixture analysis for survey nonresponse. Journal of Official Statistics, 27, 2, 153-180.
Little, R.J. (2012). Calibrated Bayes: an Alternative Inferential Paradigm for Official Statistics (with discussion and rejoinder). Journal of Official Statistics, 28, 3, 309-372.