James P. Hobert

3.4k total citations
68 papers, 2.1k citations indexed

About

James P. Hobert is a scholar working on Statistics and Probability, Artificial Intelligence and Mathematical Physics. According to data from OpenAlex, James P. Hobert has authored 68 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Statistics and Probability, 43 papers in Artificial Intelligence and 4 papers in Mathematical Physics. Recurrent topics in James P. Hobert's work include Markov Chains and Monte Carlo Methods (43 papers), Bayesian Methods and Mixture Models (41 papers) and Statistical Methods and Inference (37 papers). James P. Hobert is often cited by papers focused on Markov Chains and Monte Carlo Methods (43 papers), Bayesian Methods and Mixture Models (41 papers) and Statistical Methods and Inference (37 papers). James P. Hobert collaborates with scholars based in United States, France and Australia. James P. Hobert's co-authors include James G. Booth, George Casella, Galin L. Jones, James G. Booth, Vivekananda Roy, Kshitij Khare, Brian Caffo, Alan Agresti, Christian P. Robert and Herwig Friedl and has published in prestigious journals such as Journal of the American Statistical Association, Biometrika and Journal of the Royal Statistical Society Series B (Statistical Methodology).

In The Last Decade

James P. Hobert

66 papers receiving 2.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
James P. Hobert United States 22 1.5k 912 213 196 142 68 2.1k
Steven N. MacEachern United States 18 1.3k 0.9× 1.3k 1.5× 141 0.7× 150 0.8× 134 0.9× 79 2.1k
Chuanhai Liu United States 21 1.2k 0.8× 792 0.9× 124 0.6× 177 0.9× 69 0.5× 65 2.0k
Bent Jørgensen Denmark 26 1.4k 0.9× 665 0.7× 358 1.7× 429 2.2× 67 0.5× 66 2.7k
Lixing Zhu China 27 2.5k 1.6× 627 0.7× 353 1.7× 177 0.9× 124 0.9× 190 3.0k
Chris A. J. Klaassen Netherlands 15 1.8k 1.2× 572 0.6× 296 1.4× 139 0.7× 115 0.8× 70 2.5k
Jeffrey D. Hart United States 29 1.8k 1.2× 506 0.6× 319 1.5× 288 1.5× 82 0.6× 88 2.9k
Yingcun Xia Singapore 27 2.0k 1.3× 519 0.6× 450 2.1× 169 0.9× 189 1.3× 76 2.9k
Sadanori Konishi Japan 20 898 0.6× 469 0.5× 111 0.5× 167 0.9× 60 0.4× 88 1.9k
James G. Scott United States 23 1.3k 0.9× 955 1.0× 270 1.3× 199 1.0× 202 1.4× 65 3.2k
Πέτρος Δελλαπόρτας Greece 21 764 0.5× 585 0.6× 343 1.6× 163 0.8× 56 0.4× 77 1.7k

Countries citing papers authored by James P. Hobert

Since Specialization
Citations

This map shows the geographic impact of James P. Hobert's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by James P. Hobert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James P. Hobert more than expected).

Fields of papers citing papers by James P. Hobert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James P. Hobert. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by James P. Hobert. The network helps show where James P. Hobert may publish in the future.

Co-authorship network of co-authors of James P. Hobert

This figure shows the co-authorship network connecting the top 25 collaborators of James P. Hobert. A scholar is included among the top collaborators of James P. Hobert based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with James P. Hobert. James P. Hobert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hobert, James P., et al.. (2019). Estimating the spectral gap of a trace-class Markov operator. Electronic Journal of Statistics. 13(1). 3 indexed citations
2.
Hobert, James P., et al.. (2018). Wasserstein-based methods for convergence complexity analysis of MCMC with application to Albert and Chib's algorithm. arXiv (Cornell University). 2 indexed citations
3.
Hobert, James P., et al.. (2018). Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors. Journal of Multivariate Analysis. 166. 335–345. 5 indexed citations
4.
Hobert, James P., et al.. (2016). A comparison theorem for data augmentation algorithms with applications. Electronic Journal of Statistics. 10(1). 1 indexed citations
5.
Hobert, James P., et al.. (2014). Geometric ergodicity of Gibbs samplers for Bayesian general linear mixed models with proper priors. Linear Algebra and its Applications. 473. 54–77. 7 indexed citations
6.
Hobert, James P., et al.. (2014). Spectral properties of MCMC algorithms for Bayesian linear regression with generalized hyperbolic errors. Statistics & Probability Letters. 95. 92–100. 6 indexed citations
7.
Hobert, James P., et al.. (2013). Analysis of MCMC algorithms for Bayesian linear regression with Laplace errors. Journal of Multivariate Analysis. 117. 32–40. 14 indexed citations
8.
Robert, Christian P. & James P. Hobert. (2013). Moralizing perfect sampling. Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) (University Paris-Dauphine).
9.
Khare, Kshitij & James P. Hobert. (2012). Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression. Journal of Multivariate Analysis. 112. 108–116. 23 indexed citations
10.
Hobert, James P., et al.. (2011). Comment. Journal of Computational and Graphical Statistics. 20(3). 571–580. 3 indexed citations
11.
Roy, Vivekananda & James P. Hobert. (2010). On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors. Journal of Multivariate Analysis. 101(5). 1190–1202. 16 indexed citations
12.
Hobert, James P., et al.. (2004). Stability of the tail Markov chain and the evaluation of improper priors for an exponential rate parameter. Bernoulli. 10(3). 1 indexed citations
13.
Hobert, James P. & Christian P. Robert. (2004). A mixture representation of π with applications in Markov chain Monte Carlo and perfect sampling. The Annals of Applied Probability. 14(3). 16 indexed citations
14.
Hobert, James P.. (2001). Discussion. Journal of Computational and Graphical Statistics. 10(1). 59–68. 3 indexed citations
15.
Hobert, James P. & Christian P. Robert. (1999). Eaton's Markov chain, its conjugate partner and $\mathscr{P}$-admissibility. The Annals of Statistics. 27(1). 7 indexed citations
16.
Hobert, James P. & George Casella. (1998). Functional Compatibility, Markov Chains, and Gibbs Sampling with Improper Posteriors. Journal of Computational and Graphical Statistics. 7(1). 42–60. 38 indexed citations
17.
Hobert, James P. & Charles J. Geyer. (1998). Geometric Ergodicity of Gibbs and Block Gibbs Samplers for a Hierarchical Random Effects Model. Journal of Multivariate Analysis. 67(2). 414–430. 44 indexed citations
18.
Hobert, James P., Naomi Altman, & Carl L. Schofield. (1997). Analyses of Fish Species Richness with Spatial Covariate. Journal of the American Statistical Association. 92(439). 846–854. 25 indexed citations
19.
Hobert, James P., et al.. (1997). Connectedness conditions for the convergence of the Gibbs sampler. Statistics & Probability Letters. 33(3). 235–240. 6 indexed citations
20.
Hobert, James P. & George Casella. (1996). The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models. Journal of the American Statistical Association. 91(436). 1461–1473. 293 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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