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.
Research Interests:
Bayesian Survey Inference, Survey Nonresponse, Missing Data, Applications of Statistics in Public Health, Medicine, Government.
Selected Publications:
Little, R.J.A. & Rubin, D.B. (2002). Statistical Analysis with Missing Data 2nd edition, (1st edition 1987, 3rd edition in press), New York: John Wiley
Andridge, R.H. & Little, R.J. (2011). Proxy pattern-mixture analysis for survey nonresponse. Journal of Official Statistics, 27, 2, 153-180.
Giusti, C. & Little, R.J. (2011). An analysis of nonignorable nonresponse to income in a survey with a rotating panel design. Journal of Official Statistics, 27, 2, 211-229. {10}
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 {47}.
Little, R.J., Rubin, D.B. and Zanganeh, S.Z. (2016). Conditions for ignoring the missing-data mechanism in likelihood inferences for parameter subsets. Journal of the American Statistical Association, 112 (517), 314-320. DOI: 10.1080/01621459.2015.1136826
Current Research Projects:
Indices of Selection Bias for Non-Probability Samples