BIO：

Rod 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. He is also the Richard D. Remington Distinguished University Professor of Biostatistics; Professor of the Department of Statistics; Research Professor of the Institute for Social Research and Senior Fellow  in Michigan Society of Fellows.

He is 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.

His 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.

Abstract:

Summary Little and An (2004, Statistica Sinica 14, 949–968) proposed a penalized spline of propensity prediction (PSPP) method of imputation of missing values that yields robust model‐based inference under the missing at random assumption. The propensity score for a missing variable is estimated and a regression model is fitted that includes the spline of the estimated logit propensity score as a covariate. The predicted unconditional mean of the missing variable has a double robustness (DR) property under misspecification of the imputation model. We show that a simplified version of PSPP, which does not center other regressors prior to including them in the prediction model, also has the DR property. We also propose two extensions of PSPP, namely, stratified PSPP and bivariate PSPP, that extend the DR property to inferences about conditional means. These extended PSPP methods are compared with the PSPP method and simple alternatives in a simulation study and applied to an online weight loss study conducted by Kaiser Permanente.