Statistical Analysis with Missing Data
Empirical studies in the social, behavioral, economic, and medical sciences frequently suffer from missing data. For instance, sample surveys often have some individuals who either refuse to participate or do not supply answers to certain questions in surveys, panel surveys, and longitudinal studies often have incomplete data due to attrition. Simple approaches to handling the missing data, such as discarding incomplete cases or filling in estimates of the missing values, often yield biased or inefficient statistical inferences. Faculty in Biostatistics work on developing better methods for analyzing missing data, using models for the data and missing data mechanism, and computational tools such as the EM algorithm and the Gibbs' sampler.
Faculty: P. Boonstra, M. Elliott, P. Han, N. Kaciroti, J. Lepkowski, R. Little, B. Mukherjee, P. Song, J. Taylor, L. Wang, Z. Wu, M. Zhang