scispace - formally typeset
Search or ask a question
Author

Donald B. Rubin

Other affiliations: University of Chicago, Harvard University, Princeton University  ...read more
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.


Papers
More filters
01 Jan 1996
TL;DR: The Third National Health and Nutritional Examination Survey (NHANES III) was designed to provide statistics on the health and nutritional status of the civilian, noninstitutionalized U.S. population aged 2 months and older.
Abstract: The Third National Health and Nutritional Examination Survey (NHANES III) was designed to provide statistics on the health and nutritional status of the civilian, noninstitutionalized U.S. population aged 2 months and older. It is the seventh in a series of similar surveys conducted periodically by the National Center for Health Statistics (NCHS). Data were collected over two three-year periods, 1988–91 (Phase 1) and 1991–94 (Phase 2), with a total sample size of approximately 40,000; national estimates are produced for each three-year period and for the entire six years. NHANES III is a complex, multistage area sample with oversampling of young children (under 5), the elderly (60+) Mexican Americans and African Americans; details of the design are given by Ezzati et al. (1992).

40 citations

Journal ArticleDOI
TL;DR: A modified general location model is proposed to integrate the ideas of missing data techniques and principal stratification and then analyze the same data as in Barnard, Frangakis, Hill, and Rubin (2003), where a pattern-mixture model was used.
Abstract: Missing data, especially when coupled with noncompliance, are a challenge even in the setting of randomized experiments. Although some existing methods can address each complication, it can be diff...

40 citations

Journal ArticleDOI
TL;DR: It is shown that, in general in this setting, administrative censoring times are not independent of survival times within the two subgroups, nondropouts and sampled dropouts, and the stratified Kaplan-Meier estimator is not appropriate for the cohort survival curve.
Abstract: We thank the editorial board for the opportunity to have discussion on the issue of study design, which is often more important than analysis for obtaining reliable information, especially in problems with missing data. Double sampling designs to address dropout require allocating resources for recovering data for a subgroup of dropouts, but there are often positive trade-offs when doing so. Although such ideas have long been used for surveys with onetime enrollment, we have little evidence that they are being used systematically in studies in public health, where enrollment is often longitudinal. The conclusion is that, in these longitudinal settings, either double sampling is not often used or, as we suspect based on communications, it is employed implicitly, and the data are being analyzed with unknown methods. Our goals therefore were to

39 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
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.

50,607 citations

Book
18 Nov 2016
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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.

38,208 citations

Journal ArticleDOI
TL;DR: This paper examines eight published reviews each reporting results from several related trials in order to evaluate the efficacy of a certain treatment for a specified medical condition and suggests a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.

33,234 citations

Journal ArticleDOI
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
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.

30,570 citations