Results 241 to 250 of about 297,937 (262)
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FREQUENTIST VALIDITY OF HIGHEST POSTERIOR DENSITY REGIONS IN THE PRESENCE OF NUISANCE PARAMETERS
Statistics & Risk Modeling, 1995Summary: Priors ensuring frequentist validity, up to \(o(n^{-1})\), of credible regions based on the highest posterior density have been characterized in the presence of nuisance parameters. In this connection, the consequences of an orthogonal parametrization have also been discussed.
Ghosh, Jayanta K., Mukerjee, Rahul
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Calculating the Content and Boundary of the Highest Posterior Density Region via Data Augmentation
Biometrika, 1990SUMMARY This note considers the computation of the content and the boundary of the highest posterior density region in the context of data augmentation. The notion of the highest posterior density region and the data augmentation algorithm are reviewed.
GREG C. G. WEI, MARTIN A. TANNER
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Highest posterior density estimation from multiply censored Pareto data
Statistical Papers, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Arturo J Fernandez
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Statistics & Probability Letters, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chang, In Hong, Mukerjee, Rahul
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chang, In Hong, Mukerjee, Rahul
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Journal of Veterinary Pharmacology and Therapeutics, 1991
In this note it is argued that the principal characteristic of the confidence intervals proposed by Bartoszynski & Powers (1990) is not primarily the fact that they are of minimum length but that they are Bayesian highest posterior density intervals. A simple iterative process for determining the ends of the interval is presented.
Andrew P Grieve
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In this note it is argued that the principal characteristic of the confidence intervals proposed by Bartoszynski & Powers (1990) is not primarily the fact that they are of minimum length but that they are Bayesian highest posterior density intervals. A simple iterative process for determining the ends of the interval is presented.
Andrew P Grieve
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Computation of the highest posterior density interval in bayesian analysis
Journal of Statistical Computation and Simulation, 1993Noyan Turkkan, T. Pham-Gia
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Sample Size Calculations for Binomial Proportions via Highest Posterior Density Intervals
The Statistician, 1995Three different Bayesian approaches to sample size calculations based on highest posterior density (HPD) intervals are discussed and illustrated in the context of a binomial experiment. The preposterior marginal distribution of the data is used to find the sample size needed to attain an expected HPD coverage probability for a given fixed interval ...
Lawrence Joseph +2 more
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Computing the Bayesian highest posterior density credible sets for the lognormal mean
Environmetrics, 2002AbstractContaminant concentration data collected at Superfund sites are typically positively skewed, and the log‐normal distribution is commonly used to model such data distribution. U.S. EPA guidance documents recommend the use of H‐statistics to compute the upper confidence limit (UCL) of the mean of a log‐normal distribution. Recent work reported in
Rohan Dalpatadu, L. Gewali, A. K. Singh
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Matching Priors for Highest Posterior Density Regions
2004Highest posterior density (HPD) regions are very popular with Bayesians. With a possibly multidimensional interest parameter θ, such a region is of the form $$\{\tilde\theta: \pi (\tilde{\theta}|X) \geq K\}$$ , where \(\pi (\tilde{\theta}|X)\) is the posterior density of θ, under a prior π(·), given the data X, and K depends on π(·) and X in ...
Gauri Sankar Datta, Rahul Mukerjee
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A Note on the Construction of Highest Posterior Density Intervals
Applied Statistics, 1986This note deals with the numerical construction of highest posterior density intervals and the related problem of evaluating tail area probabilities. The methods described are applicable to univariate unimodal probability density functions. The problem of making inferences about the spread of a normal distribution is used as an example.
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