Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist

By S. Depaoli, R. van de Schoot. Psychological methods, 2017

383 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/232cadce7992b919589966dc1e92e58a4ef4aa43

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. K. Hau
2. Yue Xiao
3. Hongyun Liu
4. Daniel M. McNeish
5. Eldad Davidov

In Statistics and Probability

1. K. Hau
2. Yue Xiao
3. Hongyun Liu
4. Daniel M. McNeish
5. Francis L. Huang
6. Herbert W. Marsh
7. Milica Miočević
8. R. van de Schoot

Citations in the corpus

• On Using Bayesian Methods to Address Small Sample Problems (2016, methodology): “…advantages of Bayesian methods over frequentist maximum likelihood (ML) methods with small samples (e.g., Baldwin & Fellingham, 2013; Depaoli, 2013; Depaoli & van de Schoot, 2015; Dunson, 2000; Gelman, 2006; Hox, van de Schoot, & Matthijsse, 2012; Kadane, 2015; Lambert, Sutton, Burton, Abrams, &…”
• A Comparison of CFA, ESEM, and BSEM in Test Structure Analysis (2019, methodology): “We should be aware that all the benefits of BSEM are probably at the cost of the correctly specified priors (Depaoli & van de Schoot, 2017; Zondervan-Zwijnenburg, Peeters, Depaoli, & Van de Schoot, 2017).”