Journal ArticleDOI
Bayes and Empirical Bayes Methods for Data Analysis
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In this article, Bayes and empirical Bayes methods for data analysis are presented for Data Analysis. But, they do not consider the use of data augmentation in data analysis.Abstract:
(1997). Bayes and Empirical Bayes Methods for Data Analysis. Technometrics: Vol. 39, No. 3, pp. 337-337.read more
Citations
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Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
Journal ArticleDOI
General methods for monitoring convergence of iterative simulations
Stephen P. Brooks,Andrew Gelman +1 more
TL;DR: This work generalizes the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence.
Book
Bayes and Empirical Bayes Methods for Data Analysis
TL;DR: Approaches for Statistical Inference: The Bayes Approach, Model Criticism and Selection, and Performance of Bayes Procedures.
Journal ArticleDOI
Ozone and short-term mortality in 95 US urban communities, 1987-2000.
TL;DR: A statistically significant association between short-term changes in ozone and mortality on average for 95 large US urban communities, which include about 40% of the total US population, indicates that this widespread pollutant adversely affects public health.
Journal ArticleDOI
Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects
TL;DR: The adaptive quadrature approach is extended to general random coefficient models with limited and discrete dependent variables, which can include several nested random effects representing unobserved heterogeneity at different levels of a hierarchical dataset.
References
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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.
Book
Introduction to Statistical Inference
TL;DR: In a typical problem of statistics, it is not a single underlying probability law which is specified, but rather a class of laws, any of which may possibly be the one which actually governs the chance device or experiment whose outcome we shall observe.