MISSING DATA

Wednesday, May 17, 2006
9 am to 4 pm

York University
Toronto, Canada

Technology Enhanced Learning Building

Room 0001

Dr. Rod Little is Richard D. Remington Collegiate Professor in the Biostatistics Department and the Department of Statistics at the University of Michigan, Ann Arbor. His research interests cover a number of areas: incomplete data, sample surveys, Bayesian statistics and applied statistics.

Professor Little notes that many statistical techniques are designed for complete, rectangular data sets, but in practice biostatistical data sets contain missing values, either by design or accident. The analysis of data sets with such characteristics is a central concern for one of his major research areas. As detailed in his book with Donald Rubin, (Statistical Analysis with Missing Data, New York: John Wiley, 2nd Edition, 2002), many 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, Dr. Little has 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.

Rod Little believes that “statistics is philosophically fascinating and diverse in application”. His inferential philosophy is model-based and Bayesian, stressing that the effects of model misspecification need careful attention. Professor Little’s applied interests are broad, including mental health, demography, environmental statistics, biology, economics and the social sciences, as well as biostatistics.

For further information, please visit Dr. Little's website (http://sitemaker.umich.edu/rlittle) .