Roderick Little

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Roderick Joseph Little, PhD, is the Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, Research Professor, Institute for Social Research, and Senior Fellow, Michigan Society of Fellows, at the University of Michigan, Ann Arbor.

Prof. Little’s┬áprimary 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.

Moulinath Banerjee

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Moulinath Banerjee, PhD, is Professor of Statistics, College of Literature, Science, and the Arts, at the University of Michigan, Ann Arbor.

Moulinath Banerjee was born and raised in India where he completed both his Bachelors and Masters in Statistics at the Indian Statistical Institute, Kolkata. He obtained his Ph.D. from the Statistics department at University of Washington, Seattle, in December 2000, served as lecturer there for Winter and Spring quarters, 2001, and joined University of Michigan in Fall 2001. Mouli’s research interests are in the fields of non-standard asymptotics, empirical process theory, threshold and boundary estimation, and graphical networks. His main contributions to date are in the areas of inference under shape-restrictions, especially monotone functions, and inference in the setting of designed multistage procedures. Mouli is the recipient of the 2011 IISA Young Investigators Award and an elected fellow of ISI. He has a broad range of interests outside of statistics which include classical music, literature, history, philosophy, physics and ancestral genetics. He is, also, most emphatically, a gourmet and believes that a life without good food is a life less lived.