Donald B. Rubin

Bio: Donald B. Rubin is an academic researcher from Tsinghua University. The author has contributed to research in topics: Causal inference & Missing data. The author has an hindex of 132, co-authored 515 publications receiving 262632 citations. Previous affiliations of Donald B. Rubin include University of Chicago & Harvard University.

Statistical issues: a roundtable discussion

Abstract: Dr Alderson: I am Norris Alderson. I am Associate Commissioner for Science at FDA, and I had the privilege of working with a Planning Committee to arrange for this workshop. I was the only non-statistician in the group, I must tell you, and this was really an experience for me. I want to introduce this panel because I think it is unique from the perspective that it has representatives from FDA, industry, and academia. They are Dr Susan Ellenberg from CBER, Dr Jay Siegel from Centocor, Professor Don Rubin from Harvard University, Dr Gregory Campbell from CBER, Dr Stacy Lindborg from Eli Lilly, Dr Robert O’Neill from CDER, and Professor Ralph D’Agostino, Boston University. Our task when we set up this panel was to give this group the opportunity to summarize, from their perspective, what they have heard, and also think about what is next. Speaking on behalf of the Planning Committee, we are interested in questions, as well as comments from the audience, as related to the use of Bayesian methods as a tool, particularly in the context of the critical path initiative, reduce the time for review and approval of new public health products.

Measuring the appropriateness of multiple‐choice test scores1,2

Abstract: A student may be so atypical and unlike other students that his or her aptitude test score fails to be a completely appropriate measure of his or her relative ability. We consider the problem of using the student's pattern of multiplechoice aptitude test answers to decide whether his or her score is an appropriate ability measure. Several indicators of appropriateness are formulated and evaluated with a simulation of the Scholastic Aptitude Test. Applications to investigations of alignment errors, exceptional creativity, suboptimal test taking strategies, and unauthorized access to test items are noted.

On optimal re-randomization designs

Abstract: Blocking is commonly used in randomized experiments to increase efficiency of estimation. A generalization of blocking is to remove allocations with imbalance in covariates between treated and cont ...

Taking causality seriously: propensity score methodology applied to estimate the effects of marketing interventions

Abstract: Propensity score methods were proposed by Rosenbaum and Rubin (1983, Biometrika) as central tools to help assess the causal effects of interventions Since their introduction two decades ago, they have found wide application in a variety of areas, including medical research, economics, epidemiology, and education, especially in those situations where randomized experiments are either difficult to perform, or raise ethical questions, or would require extensive delays before answers could be obtained Rubin (1997, Annals of Internal Medicine) provides an introduction to some of the essential ideas In the past few years, the number of published applications using propensity score methods to evaluate medical and epidemiological interventions has increased dramatically Rubin (2003, Erlbaum) provides a summary, which is already out of date

Fitting Linear Mixed-Effects Models Using lme4

Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.

Deep Learning

Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Latent dirichlet allocation

Abstract: We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.