Professional Summary
Dr. Little became Chair of the Biostatistics Department in January 2007, having previously chaired the Department 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. 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. He has over 150 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. In 2005 Dr. Little received the Wilks' Memorial Award from the American Statistical Association for his research contributions.
Courses Taught
BIOSTAT590: Statistical analysis and presentation of research projects
BIOSTAT599: Planning and Funding Clinical Research
BIOSTAT880: Statistical Analysis With Missing Data
Education
Ph.D., Statistics, London University, 1974
M.Sc., Statistics and Operational Research, London University, 1972
B.A., Mathematics, Cambridge University, 1971
Research Interest & Projects
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.
Selected Publications
Andridge, R.H. & Little, R.J. (2008). The Use of Sample Weights in Hot Deck Imputation. Journal of Official Statistics, In press
Little, R.J., Long, Q., Lin, X." (2008). A Comparison of Methods for Estimating the Causal Effect of a Treatment in Randomized Clinical Trials Subject to Noncompliance Biometrics, In press
Zhang, G., Little, R.J." (2008). Extensions of the Penalized Spline of Propensity Prediction Method of Imputation Biometrics, In press
Long, Q., Little, R.J., and Lin, X." (2008). Causal Inference in Hybrid Intervention Studies Involving Treatment Choice. Journal of the American Statistical Association, 103, 474-484.
Yuan, Y., Little, R.J." (2007). Parametric and Semiparametric Model-Based Estimates of the Finite Population Mean for Two-Stage Cluster Samples with Item Nonresponse Biometrics, 63, 1172-1180.
Yuan, Y., Little, R.J." (2007). Model-Based Estimates of the Finite Population Mean for Two-Stage Cluster Samples with Unit Nonresponse Journal of the Royal Statistical Society, Ser. C., 56, 79-97.
Little, R.J.A. (2006). Calibrated Bayes: A Bayes/Frequentist Roadmap. The American Statistician, 60 3, 213-223.
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.
Little, R.J.A. and An, Hyonggin (2004). Robust likelihood-based analysis of multivariate data with missing values. Statistica Sinica, 14, 949-968.
Little, RJA and Rubin, DB (2002). Statistical Analysis with Missing Data. New York: John Wiley, 2nd Edition.
Professional Affiliations
Fellow, American Statistical Association
Member, International Biometrics Society
Fellow, Royal Statistical Society
Member, International Statistical Institute
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