Roderick J Little
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 is currently co-editor of the Journal of Survey Statistics and Methodology. From Jan 2010-Dec 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 Sep 2010-Jan 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 200 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 29 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.
- Roderick J Little. 2023. Some Reflections on Rosenbaum and Rubin's Propensity Score Paper. Observational Studies 9(1):69-75.
- Yajuan Si, Steven G Heeringa, Johnson, David, Roderick J Little, Liu, Wenshuo, Fabian T Pfeffer, Trivellore E Raghunathan. 2023. Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics. Journal of Survey Statistics and Methodology 11(1):260-283.
- Yu, Yuanzhi, Roderick J Little, Perzanowski, Matthew, Chen, Qixuan. 2023. Multiple imputation of more than one environmental exposure with nondifferential measurement error. Biostatistics
- Roderick J Little. 2023. Bayes, buttressed by design-based ideas, is the best overarching paradigm for sample survey inference. Survey Methodology 48(2)
- Little,Roderick J, James R. Carpenter, Katherine J. Lee. 2022. A Comparison of Three Popular Methods for Handling Missing Data: Complete-Case Analysis, Inverse Probability Weighting, and Multiple Imputation. Sociological Methods & Research
- Sahar Z. Zangeneh, Roderick J Little. 2022. Likelihood-Based Inference for the Finite Population Mean with Post-Stratification Information Under Non-Ignorable Non-Response. International Statistical Review 90(S1):S17-S36.
- Zhou, Tingting, Michael R Elliott, Roderick J Little. 2022. Addressing Disparities in the Propensity Score Distributions for Treatment Comparisons from Observational Studies. Stats 5(4):1254-1270.
- Marco H. Benedetti, Veronica J. Berrocal, Roderick J. Little, Marco H. Benedetti, Veronica J. Berrocal, Roderick J Little. 2022. Accounting for survey design in Bayesian disaggregation of survey-based areal estimates of proportions: An application to the American Community Survey. The Annals of Applied Statistics 16(4):2201-2230.
- Yajuan Si, Roderick J Little, Ya Mo, Nell Sedransk. 2022. A Case Study of Nonresponse Bias Analysis in Educational Assessment Surveys. Journal of Educational and Behavioral Statistics 48(3):271-295.
- Little, Roderick J. 2021. Missing Data Assumptions. Annual Review of Statistics and Its Application 8(1):89-107.