H
Hal S. Stern
Researcher at University of California, Irvine
Publications - 155
Citations - 27126
Hal S. Stern is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Bayesian inference & Bayesian statistics. The author has an hindex of 42, co-authored 146 publications receiving 25831 citations. Previous affiliations of Hal S. Stern include Loma Linda University & Harvard University.
Papers
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Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Posterior predictive assessment of model fitness via realized discrepancies
TL;DR: In this article, the authors consider Bayesian counterparts of the classical tests for good-ness of fit and their use in judging the fit of a single Bayesian model to the observed data.
Journal ArticleDOI
The Prevention and Treatment of Missing Data in Clinical Trials
Roderick J. A. Little,Ralph B. D'Agostino,Michael L. Cohen,Kay Dickersin,Scott S. Emerson,John T. Farrar,Constantine Frangakis,Joseph W. Hogan,Geert Molenberghs,Susan A. Murphy,James D. Neaton,Andrea Rotnitzky,Daniel O. Scharfstein,Weichung Joe Shih,Jay P. Siegel,Hal S. Stern +15 more
TL;DR: Methods for preventing missing data and, failing that, dealing with data that are missing in clinical trials are reviewed.
Journal ArticleDOI
The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant
Andrew Gelman,Hal S. Stern +1 more
TL;DR: The authors pointed out that even large changes in significance levels can correspond to small, nonsignificant changes in the underlying quantities, which encourages the dismissal of observed differences in favor of the usually less interesting null hypothesis of no difference.
Journal ArticleDOI
The use of multiple imputation for the analysis of missing data.
TL;DR: The idea behind MI, the advantages of MI over existing techniques for addressing missing data, how to do MI for real problems, the software available to implement MI, and the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI are discussed.