Description
Don Rubin (1943-) is a statistician who has made major contributions in statistical modeling, computation, and the foundations of causal inference. Rubin grew up in the Chicago area, studied physics at Princeton University, and received his PhD at Harvard University in 1970. He has worked at the Educational Testing Service and has taught statistics at the University of Wisconsin, University of Chicago, and Harvard University. He is best known, perhaps, for the expectation-maximization (EM) algorithm (a mathematical framework for iterative optimization, which has been useful for mixture models, hierarchical regression, and many other problems for which closed-form solutions are unavailable); multiple imputation as a method for accounting for uncertainty in statistical analysis with missing data; a Bayesian formulation of instrumental variables analysis in economics; propensity ... (Description from external book data)
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