The Effective Sample Size

DOI10.1080/07474938.2013.807157zbMath1491.62183OpenAlexW2034844691WikidataQ63362821 ScholiaQ63362821MaRDI QIDQ5080443

M. J. Bayarri, James O. Berger, Luís Raúl Pericchi

Publication date: 31 May 2022

Published in: Econometric Reviews (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474938.2013.807157

zbMATH Keywords

model selectionlinear modelsBICcorrelated dataprior scales

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Linear regression; mixed models (62J05) Bayesian inference (62F15) Sampling theory, sample surveys (62D05)

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Prior-based Bayesian information criterion, Discussion on prior-based Bayes information criterion, Rejoinder by James Berger, Woncheol Jang, Surajit Ray, Luis R. Pericchi and Ingmar Visser, Effective sample size for spatial regression models, Bayesian alternatives to null-hypothesis significance testing for repeated-measures designs, Prior distributions for objective Bayesian analysis, Bayesian model selection for a linear model with grouped covariates, Cost-effectiveness analysis for heterogeneous samples, Mutual interference in working memory updating: a hierarchical Bayesian model, Changing statistical significance with the amount of information: the adaptive \(\alpha\) significance level, Sparse identification of truncation errors, Assessing the effective sample size for large spatial datasets: a block likelihood approach, On the effective geographic sample size, Model-based clustering for populations of networks, Restricted type II maximum likelihood priors on regression coefficients, A model selection approach for variable selection with censored data, Increasing the replicability for linear models via adaptive significance levels

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