James P. Hobert

Statistics and Probability · 2000–2013

A researcher in the Science corpus: 11 papers; trusts 25 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 57 citations into it; #1,141 of 12,932 by trust here, #1,182 by citations
• Artificial Intelligence — 37 citations into it; #4,392 of 35,345 by trust here, #8,007 by citations
• Probabilistic & Bayesian Methods — 18 citations into it
• Mathematical Physics — 13 citations into it
• Statistical Learning Theory — 10 citations into it

Whom James P. Hobert trusts overall

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

1. Galin L. Jones
2. Gareth O. Roberts
3. D. V. van Dyk
4. Jeffrey S. Rosenthal
5. Sean P. Meyn
6. Richard L. Tweedie
7. Jim Albert
8. Xiao-Li Meng
9. Omiros Papaspiliopoulos
10. Trevor Park

Who trusts James P. Hobert overall

1. Galin L. Jones
2. James M. Flegal
3. Jeffrey S. Rosenthal
4. Persi Diaconis
5. Dootika Vats
6. Radford M. Neal
7. Kshitij Khare
8. Yaming Yu
9. Gareth O. Roberts
10. Joyee Ghosh

Papers from James P. Hobert's perspective

Ordered by how strongly the researchers James P. Hobert trusts endorse each paper. Same corpus, a different order for every researcher.

1. Honest Exploration of Intractable Probability Distributions via Markov Chain Monte Carlo
2. Corrigendum to : Bounds on regeneration times and convergence rates for Markov chains
3. The Bayesian Lasso
4. Generalized Linear Models
5. Markov Chains and De‐initializing Processes
6. Markov chain Monte Carlo: Can we trust the third significant figure?
7. Batch means and spectral variance estimators in Markov chain Monte Carlo
8. Penalized regression, standard errors, and Bayesian lassos
9. Sampling-Based Approaches to Calculating Marginal Densities
10. Monte Carlo Statistical Methods

Their papers

• Honest Exploration of Intractable Probability Distributions via Markov Chain Monte Carlo (2001) — 335 citations
• On the applicability of regenerative simulation in Markov chain Monte Carlo (2002) — 139 citations
• Sufficient burn-in for Gibbs samplers for a hierarchical random effects model (2004) — 117 citations
• Geometric Ergodicity of van Dyk and Meng's Algorithm for the Multivariate Student's t Model (2004) — 56 citations
• Geometric ergodicity of the Bayesian lasso (2013) — 49 citations
• Block Gibbs Sampling for Bayesian Random Effects Models With Improper Priors: Convergence and Regeneration (2009) — 51 citations
• Convergence rates and asymptotic standard errors for Markov chain Monte Carlo algorithms for Bayesian probit regression (2007) — 90 citations
• Negative binomial loglinear mixed models (2003) — 135 citations
• A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms (2008) — 92 citations
• 2. Random-Effects Modeling of Categorical Response Data (2000) — 141 citations
• A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants (2011) — 49 citations