Roderick Joseph Little, PhD
- Richard D. Remington Distinguished University Professor of Biostatistics
- Professor, Department of Statistics
- Research Professor, Institute for Social Research
Rod Little chaired the Biostatistics Department from January 2007 to December 2009, and from 1993 to 2001. Prior to that he was Professor in the Department of Biomathematics at the University of California at Los Angeles; Research Fellow at the U.S. Bureau of the Census (1982-83); Expert Consultant at the United States Environmental Protection Agency; Scientific Associate at the World Fertility Survey; and Research Associate (Assistant Professor) in the Department of Statistics, University of Chicago.
Active editorially, he was Coordinating and Applications Editor of the Journal of the American Statistical Association from 1992-1994, and he was co-editor of the Journal of Survey Statistics and Methodology from 2016 to 2018. From January 2010-December 2012, Little was a Vice President of the American Statistical Association.
Since his fellowship at the Census Bureau he has been interested in federal statistical issues such as the census undercount, and he has served as a member of the Committee on National Statistics and a number of other National Research Council committees. In 2009-10 he chaired an NRC study on the prevention and treatment of missing data in clinical trials. From September 2010-January 2013, Little served as the inaugural Associate Director for Research and Methodology and Chief Scientist at the U.S. Census Bureau.
An ISI highly cited researcher, he has over 250 refereed publications, notably on methods for the analysis of data with missing values and model-based survey inference, and the application of statistics to diverse scientific areas, including medicine, demography, economics, psychiatry, aging and the environment. He has chaired or co-chaired 30 doctoral committees. In 2005 Dr. Little received the Wilks' Memorial Award from the American Statistical Association for his research contributions. At the Joint Statistical Meetings, he gave the President's Invited Address in 2005 and the COPSS Fisher lecture in 2012.
Little is a Fellow of the American Statistical Association and the American Academy of Arts and Sciences, and a member of the National Academy of Medicine.
1985-Present: Fellow, American Statistical Association
Member, International Biometrics Society
Fellow, Royal Statistical Society
Member, International Statistical Institute
2010-Present: Fellow, American Academy of Arts and Sciences
2011-2015: Member, Institute of Medicine of the National Academy of Sciences
2015-present: Member, National Academy of Medicine
- PhD, Statistics, London University, 1974
- MSc, Statistics and Operational Research, London University, 1972
- BA, Mathematics, Cambridge University, 1971
A primary research interest is the analysis of data sets with missing values. Many statistical techniques are designed for complete, rectangular data sets, but in practice biostatistical data sets contain missing values, either by design or accident. As detailed in my book with Rubin, initial statistical approaches were relatively ad-hoc, such as discarding incomplete cases or substituting means, but modern methods are increasingly based on models for the data and missing-data mechanism, using likelihood-based inferential techniques.
Another interest is the analysis of data collected by complex sampling designs involving stratification and clustering of units. Since working as a statistician for the World Fertility Survey, I have been interested in the development of model-based methods for survey analysis that are robust to misspecification, reasonably efficient, and capable of implementation in applied settings. Statistics is philosophically fascinating and diverse in application. My inferential philosophy is model-based and Bayesian, although the effects of model misspecification need careful attention. My applied interests are broad, including mental health, demography, environmental statistics, biology, economics and the social sciences as well as biostatistics.
West, B.T., Little, R.J., Andridge, R.R., Boonstra, P.S., Ware, E.B., Pandit, A., Alvarado-Leiton, F. (2021). Measures of Selection Bias in Regression Coefficients Estimated from Non-Probability Samples. The Annals of Applied Statistics, 15 (3), 1556-1581.
Little, R.J. and Lewis, R.J. (2021). Estimands, Estimators and Estimates. Journal of the American Medical Association, 326, 10, 967-968.
Little, R.J. (2021). Missing Data Assumptions. Annual Review of Statistics and Its Application, 8, 89-107.
Andridge, R.R. and Little, R.J.A. (2020). Proxy Pattern-Mixture Analysis for a Binary Variable Subject to Nonresponse. Journal of Official Statistics, 36, 3, 703-728. http://dx.doi.org/10.2478/JOS- 2020-0035.
Little, R.J., West, B.T., Boonstra, P.S. and Hu, J. (2020). Measures of the Degree of Departure from Ignorable Sample Selection. Journal of Survey Statistics and Methodology, 8, 5, 932-964.
Little, R.J.A. and Rubin, D.B. (2019). Statistical Analysis with Missing Data 3rd edition April 2019 (2nd edition 2002, 1st edition 1987), New York: John Wiley
Zhou, T., Elliott, M.R. and Little, R.J. (2019). Penalized Spline of Propensity Methods for Treatment Comparisons (with discussion and rejoinder). Journal of the American Statistical Association, 114:525, 1-38, DOI: 10.1080/01621459.2018.1518234.
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
Little, R.J. and Kang, S. (2015). Intention-to-Treat Analysis with Treatment Discontinuation and Missing Data in Clinical Trials. Statistics in Medicine, 34, 16, 2381-2390. DOI: 10.1002/sim.6352
Little, R.J. (2013). In Praise of Simplicity, Not Mathematistry! Simple, Powerful Ideas for the Applied Statistician. Journal of the American Statistical Association, 108, 359-370.