Roderick Little

photo of Roderick Little
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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