Search results for "posterior moments and cumulant"

showing 2 items of 2 documents

Sampling properties of the Bayesian posterior mean with an application to WALS estimation

2022

Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these learning methods in repeated samples is assessed using the variance of the posterior distribution of the parameters of interest given the data. This may be permissible when the sample size is large because, under the conditions of the Bernstein--von Mises theorem, the posterior variance agrees asymptotically with the frequentist variance. In finite samples, however, things are less clear. In this pa…

Economics and EconometricsWALS.SDG 16 - PeaceSettore SECS-P/05Monte Carlo methodBayesian probabilityPosterior probabilitySettore SECS-P/05 - EconometriaDouble-shrinkage estimators01 natural sciencesLeast squares010104 statistics & probabilityFrequentist inference0502 economics and businessStatisticsPosterior moments and cumulantsStatistics::Methodology0101 mathematicsdouble-shrinkage estimator050205 econometrics MathematicsWALSLocation modelApplied Mathematics05 social sciencesSDG 16 - Peace Justice and Strong InstitutionsUnivariateSampling (statistics)EstimatorVariance (accounting)/dk/atira/pure/sustainabledevelopmentgoals/peace_justice_and_strong_institutionsJustice and Strong InstitutionsSample size determinationposterior moments and cumulantNormal location modelJournal of Econometrics
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Posterior moments and quantiles for the normal location model with Laplace prior

2021

We derive explicit expressions for arbitrary moments and quantiles of the posterior distribution of the location parameter η in the normal location model with Laplace prior, and use the results to approximate the posterior distribution of sums of independent copies of η.

Statistics and ProbabilityLaplace priorsLaplace priorLocation parameterreflected generalized gamma priorSettore SECS-P/05Posterior probability0211 other engineering and technologiesSettore SECS-P/05 - Econometria02 engineering and technology01 natural sciencesCornish-Fisher approximation010104 statistics & probabilityStatistics::Methodologyposterior quantile0101 mathematicsposterior moments and cumulantsMathematicsreflected generalized gamma priors021103 operations researchLaplace transformLocation modelMathematical analysisStatistics::Computationposterior moments and cumulantCornish–Fisher approximationSettore SECS-S/01 - StatisticaNormal location modelposterior quantilesQuantileCommunications in Statistics - Theory and Methods
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