Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: a comment on Nathoo, Kilshaw, and Masson (2018)
type: Item
instance of: scholarly article
title: Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: a comment on Nathoo, Kilshaw, and Masson (2018)
zbMATH Open document ID: 1416.62424
author: Daniel W. Heck
published in: Journal of Mathematical Psychology
publication date: 2019-03-29T00:00:00Z
full work available at URL: whp8t
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 7044915
zbMATH Keywords: repeated-measures designs
zbMATH Keywords: linear mixed model
zbMATH Keywords: credible interval
zbMATH Keywords: highest-density interval
zbMATH Keywords: within-subject Bayesian inference
describes a project that uses: Stan
describes a project that uses: BayesDA
describes a project that uses: TreeBUGS
describes a project that uses: JAGS
describes a project that uses: S-PLUS
describes a project that uses: lme4
describes a project that uses: MEMSS
MaRDI profile type: Publication
OpenAlex ID: W2902847526
cites work: Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)
cites work: Q3185327
cites work: What can we learn from plausible values?
cites work: A better (Bayesian) interval estimate for within-subject designs
cites work: Mixed-Effects Models in S and S-PLUS
cites work: Default Bayes factors for ANOVA designs
cites work: Q3237829
DOI: 10.1016/J.JMP.2018.11.002
description: scientific article; zbMATH DE number 7044915