Jeffrey N. Rouder

Statistics and Probability · 2003–2019

A researcher in the Science corpus: 18 papers; trusts 42 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 113 citations into it; #1,772 of 12,932 by trust here, #907 by citations
• Artificial Intelligence — 53 citations into it; #6,911 of 35,345 by trust here, #11,921 by citations
• Experimental and Cognitive Psychology — 27 citations into it
• Cognitive Neuroscience — 22 citations into it; #1,727 of 10,852 by trust here, #3,875 by citations
• Management Science and Operations Research — 22 citations into it; #4,848 of 11,946 by trust here, #4,267 by citations
• General Decision Sciences — 17 citations into it; #555 of 1,995 by trust here, #273 by citations
• Developmental and Educational Psychology — 13 citations into it; #1,229 of 8,223 by trust here, #3,615 by citations
• History and Philosophy of Science — 11 citations into it; #368 of 602 by trust here, #310 by citations

Whom Jeffrey N. Rouder trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Andrew Gelman
2. Hirotugu Akaike
3. Alan E. Gelfand
4. James O. Berger
5. Feng Liang
6. Herbert H. Clark
7. John K. Kruschke
8. Samuel Kotz
9. Eric-Jan Wagenmakers
10. Anthony B. Atkinson

Who trusts Jeffrey N. Rouder overall

1. Eric-Jan Wagenmakers
2. Richard D. Morey
3. Zoltan Dienes
4. John K. Kruschke
5. D. Quintana
6. Andrew F. Jarosz
7. L. DeCarlo
8. Karl Christoph Klauer
9. M. Masson
10. Donald R. Williams

Papers from Jeffrey N. Rouder's perspective

Ordered by how strongly the researchers Jeffrey N. Rouder trusts endorse each paper. Same corpus, a different order for every researcher.

1. Estimating the Dimension of a Model
2. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
3. Bayes factors
4. Sampling-Based Approaches to Calculating Marginal Densities
5. The case for objective Bayesian analysis
6. A new look at the statistical model identification
7. Estimation with Quadratic Loss
8. Subset Selection in Regression
9. Extending conventional priors for testing general hypotheses in linear models
10. Calibration of ρ Values for Testing Precise Null Hypotheses

Their papers

• Bayesian t tests for accepting and rejecting the null hypothesis (2009) — 3,569 citations
• Using MCMC chain outputs to efficiently estimate Bayes factors (2011) — 84 citations
• Default Bayes Factors for Model Selection in Regression (2012) — 455 citations
• Bayesian inference for psychology. Part II: Example applications with JASP (2017) — 1,530 citations
• Default Bayes factors for ANOVA designs (2012) — 1,707 citations
• The need for Bayesian hypothesis testing in psychological science (2017) — 129 citations
• Robust Modeling in Cognitive Science (2019) — 92 citations
• JASP: Graphical Statistical Software for Common Statistical Designs (2019) — 905 citations
• Teaching Bayes’ Theorem: Strength of Evidence as Predictive Accuracy (2018) — 47 citations
• Baysefactor: Computation of Bayes Factors for Common Designs (2018) — 1,299 citations
• Bayesian Analysis of Factorial Designs (2017) — 337 citations
• The philosophy of Bayes’ factors and the quantification of statistical evidence (2016) — 289 citations
• Computation of Bayes Factors for Common Designs (2015) — 221 citations
• How to measure working memory capacity in the change detection paradigm (2011)
• Delta Plots and Coherent Distribution Ordering (2008) — 54 citations
• An assessment of fixed-capacity models of visual working memory (2008) — 350 citations
• An introduction to Bayesian hierarchical models with an application in the theory of signal detection (2005) — 408 citations
• A hierarchical bayesian statistical framework for response time distributions (2003) — 155 citations