Robert E. Kass

Statistics and Probability · 1989–2018

A researcher in the Science corpus: 15 papers; trusts 17 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 26 citations into it; #2,084 of 12,932 by trust here, #2,183 by citations
• Cellular and Molecular Neuroscience — 25 citations into it; #852 of 2,998 by trust here, #1,353 by citations
• Cognitive Neuroscience — 24 citations into it; #3,524 of 10,852 by trust here, #3,321 by citations
• Artificial Intelligence — 16 citations into it; #11,241 of 35,345 by trust here, #16,471 by citations
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Robert E. Kass trusts overall

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

1. Denise Draper
2. Anthony B. Atkinson
3. S. Geisser
4. R. Natarajan
5. Jun S. Liu
6. Radford M. Neal
7. Mark H. Hansen
8. James O. Berger
9. Donald B. Rubin
10. Charles H. Bennett

Who trusts Robert E. Kass overall

1. Eric-Jan Wagenmakers
2. Adrian E. Raftery
3. Mark F. J. Steel
4. Valen E. Johnson
5. S. Vagnozzi
6. David E. Heckerman
7. Nathaniel D. Daw
8. Balaji Lakshminarayanan
9. Richard D. Morey
10. M. Masson

Papers from Robert E. Kass's perspective

Ordered by how strongly the researchers Robert E. Kass trusts endorse each paper. Same corpus, a different order for every researcher.

1. Estimating the Dimension of a Model
2. Generalized Linear Models
3. Bayes factors
4. Present position and potential developments: some personal views
5. Subset Selection in Regression
6. Statistical Decision Theory and Bayesian Analysis, Second Edition
7. A Predictive Approach to Model Selection
8. Monte Carlo strategies in scientific computing
9. Bayesian Model Choice: Asymptotics and Exact Calculations
10. Bayesian and likelihood-based methods in multilevel modeling 1 A comparison of Bayesian and likelihood-based methods for fitting multilevel models

Their papers

• Importance sampling: a review (2010) — 478 citations
• Reference Bayesian Methods for Generalized Linear Mixed Models (2000) — 238 citations
• Shrinkage Estimators for Covariance Matrices (2001) — 273 citations
• Functional network reorganization during learning in a brain-computer interface paradigm (2008) — 310 citations
• A default conjugate prior for variance components in generalized linear mixed models (comment on article by Browne and Draper) (2006) — 81 citations
• Bayesian curve-fitting with free-knot splines (2001) — 487 citations
• Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data (2018) — 624 citations
• Grand Challenges for Global Brain Sciences (2016)
• Statistical issues in the analysis of neuronal data. (2005) — 333 citations
• Multiple neural spike train data analysis: state-of-the-art and future challenges (2004) — 936 citations
• The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis (2002) — 601 citations
• The Physics of the B Factories (2001) — 539 citations
• Computing Bayes Factors by Combining Simulation and Asymptotic Approximations (1997)
• Bayes factors (1995)
• Approximate marginal densities of nonlinear functions (1989)