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Prior distributions for variance parameters in hierarchical models
 Bayesian Analysis
, 2006
"... Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new foldednoncentralt family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors i ..."
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Cited by 430 (15 self)
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Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new foldednoncentralt family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors
Prior Distribution
, 2000
"... prior distributions, which can themselves be estimated from data. Item for Encyclopedia of Environmetrics. This work was supported in part by the U.S. National Science Foundation grant SBR9708424 and Young Investigator Award DMS9796129. y Department of Statistics, Columbia University, New York, ..."
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Cited by 1 (1 self)
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prior distributions, which can themselves be estimated from data. Item for Encyclopedia of Environmetrics. This work was supported in part by the U.S. National Science Foundation grant SBR9708424 and Young Investigator Award DMS9796129. y Department of Statistics, Columbia University, New York
Specifying Prior Distributions of Functionals
, 1998
"... Many Bayesian analyses rely heavily on Markov Chain Monte Carlo (MCMC) techniques. MCMC techniques work fastest when the prior distribution of the parameters is chosen conveniently, usually a conjugate prior. However, this is sometimes at odds with the prior desired by the investigator. We describe ..."
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Many Bayesian analyses rely heavily on Markov Chain Monte Carlo (MCMC) techniques. MCMC techniques work fastest when the prior distribution of the parameters is chosen conveniently, usually a conjugate prior. However, this is sometimes at odds with the prior desired by the investigator. We describe
Identifying Parametric Prior Distributions
, 2008
"... In a Bayesian analysis the statistician must specify prior densities for the model parameters. If he is bold enough to choose an informative prior for the model parameter θ, then this prior should wellrepresent beliefs about θ. In a Bayesian analysis the statistician must specify prior densities fo ..."
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→ p(θ) As a start towards this end, a set of R functions have been written to identify a prior distribution for θ when a continuous parametric density can adequately represent prior beliefs about θ.
Compatible Prior Distributions
, 2001
"... this paper is to investigate possible explications of this informal concept of `compatibility'. For simplicity, we restrict attention to the case that M 0 is a lower dimensional submodel of M, and examine two methods that have commonly been used 1 in this case: `projection' and `condition ..."
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Cited by 18 (1 self)
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of these methods might be appropriate. 2. EXAMPLES We focus attention on two simple examples, each involving a random sample of n observations on a pair of outcome variables. In the first example the outcome variables have a joint Gaussian distribution, while in the second they are binary. Our notation
Prior Distributions for the Bivariate Binomial
, 1990
"... erties since we allow them to depend on the parameter of interest. Some key words: Jeffreys prior; Multivariate binomial; Nuisance parameter; Reference prior. 1. INTRODUCTION Crowder & Sweeting (1989) considered the following model. Each of m spores has a probability p of germinating. Of the ..."
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Cited by 6 (0 self)
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erties since we allow them to depend on the parameter of interest. Some key words: Jeffreys prior; Multivariate binomial; Nuisance parameter; Reference prior. 1. INTRODUCTION Crowder & Sweeting (1989) considered the following model. Each of m spores has a probability p of germinating
Infinite hierarchies and prior distributions
 Bernoulli
, 2001
"... Abstract. This paper introduces a way of constructing noninformative priors for Bayesian analysis, by taking a limit of priors arising from hierarchical constructions as the number of levels in the hierarchy converges to infinity. Results are proved showing that for location families, and other rel ..."
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Cited by 1 (0 self)
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Abstract. This paper introduces a way of constructing noninformative priors for Bayesian analysis, by taking a limit of priors arising from hierarchical constructions as the number of levels in the hierarchy converges to infinity. Results are proved showing that for location families, and other
Compatible Prior Distributions for DAG models
, 2002
"... The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requires the specification of prior distributions on the parameters of alternative models. We propose a new method for constructing compatible priors on the parameters of models nested in a given DAG (Direc ..."
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Cited by 9 (2 self)
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The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requires the specification of prior distributions on the parameters of alternative models. We propose a new method for constructing compatible priors on the parameters of models nested in a given DAG
On perturbations of strongly admissible prior distributions
, 2006
"... Consider a parametric statistical model, P(dxθ), and an improper prior distribution, ν(dθ), that together yield a (proper) formal posterior distribution, Q(dθx). The prior is called strongly admissible if the generalized Bayes estimator of every bounded function of θ is admissible under squared er ..."
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Cited by 4 (4 self)
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Consider a parametric statistical model, P(dxθ), and an improper prior distribution, ν(dθ), that together yield a (proper) formal posterior distribution, Q(dθx). The prior is called strongly admissible if the generalized Bayes estimator of every bounded function of θ is admissible under squared
Results 1  10
of
16,682