Michael D. Perlman

4.8k total citations · 1 hit paper
105 papers, 2.8k citations indexed

About

Michael D. Perlman is a scholar working on Statistics and Probability, Artificial Intelligence and Applied Mathematics. According to data from OpenAlex, Michael D. Perlman has authored 105 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Statistics and Probability, 31 papers in Artificial Intelligence and 17 papers in Applied Mathematics. Recurrent topics in Michael D. Perlman's work include Bayesian Modeling and Causal Inference (21 papers), Advanced Statistical Methods and Models (19 papers) and Statistical Methods and Bayesian Inference (17 papers). Michael D. Perlman is often cited by papers focused on Bayesian Modeling and Causal Inference (21 papers), Advanced Statistical Methods and Models (19 papers) and Statistical Methods and Bayesian Inference (17 papers). Michael D. Perlman collaborates with scholars based in United States, Canada and Denmark. Michael D. Perlman's co-authors include Steen A. Andersson, David Madigan, Leon Jay Gleser, Allan R. Sampson, S. James Press, Mathias Drton, Morris L. Eaton, Lang Wu, Somesh Das Gupta and James A. Koziol and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and Proceedings of the IEEE.

In The Last Decade

Michael D. Perlman

96 papers receiving 2.5k citations

Hit Papers

Contributions to Probability and Statistics 1989 2026 2001 2013 1989 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael D. Perlman United States 27 1.2k 940 407 365 229 105 2.8k
J. N. Darroch Australia 27 1.1k 0.9× 974 1.0× 337 0.8× 184 0.5× 213 0.9× 66 3.5k
Tim Robertson United States 25 2.3k 1.9× 785 0.8× 823 2.0× 486 1.3× 173 0.8× 98 4.4k
Merlise A. Clyde United States 23 992 0.8× 657 0.7× 258 0.6× 202 0.6× 121 0.5× 53 2.7k
Nanny Wermuth Germany 23 1.0k 0.8× 962 1.0× 228 0.6× 180 0.5× 161 0.7× 58 2.2k
Yuhong Yang United States 28 1.5k 1.2× 1.0k 1.1× 611 1.5× 206 0.6× 123 0.5× 124 3.9k
H. D. Brunk United States 19 1.1k 0.9× 557 0.6× 353 0.9× 100 0.3× 171 0.7× 50 2.3k
S. Kocherlakota Canada 16 1.3k 1.1× 609 0.6× 355 0.9× 170 0.5× 61 0.3× 48 3.1k
Ricardo Fraiman Uruguay 27 1.6k 1.4× 777 0.8× 182 0.4× 123 0.3× 93 0.4× 88 2.7k
D. J. Best Australia 21 1.1k 0.9× 460 0.5× 207 0.5× 201 0.6× 45 0.2× 120 2.9k
Jinchi Lv United States 17 2.3k 2.0× 1.1k 1.1× 283 0.7× 705 1.9× 109 0.5× 42 3.9k

Countries citing papers authored by Michael D. Perlman

Since Specialization
Citations

This map shows the geographic impact of Michael D. Perlman'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 Michael D. Perlman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael D. Perlman more than expected).

Fields of papers citing papers by Michael D. Perlman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael D. Perlman. 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 Michael D. Perlman. The network helps show where Michael D. Perlman may publish in the future.

Co-authorship network of co-authors of Michael D. Perlman

This figure shows the co-authorship network connecting the top 25 collaborators of Michael D. Perlman. A scholar is included among the top collaborators of Michael D. Perlman 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 Michael D. Perlman. Michael D. Perlman 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.
Perlman, Michael D., et al.. (2023). Socle degrees for local cohomology modules of thickenings of maximal minors and sub-maximal Pfaffians. Proceedings of the American Mathematical Society.
2.
Perlman, Michael D., et al.. (2023). Equivariant resolutions over Veronese rings. Journal of the London Mathematical Society. 109(1).
3.
Perlman, Michael D.. (2023). Mixed Hodge Structure on Local Cohomology with Support in Determinantal Varieties. International Mathematics Research Notices. 2024(1). 331–358.
4.
Perlman, Michael D., et al.. (2019). EQUIVARIANT -MODULES ON ALTERNATING SENARY 3-TENSORS. Nagoya Mathematical Journal. 243. 61–82. 2 indexed citations
5.
Perlman, Michael D.. (2019). Equivariant D-modules on 2 × 2 × 2 hypermatrices. Journal of Algebra. 544. 391–416. 2 indexed citations
6.
Chaudhuri, Sanjay & Michael D. Perlman. (2006). Two step-down tests for equality of covariance matrices. Linear Algebra and its Applications. 417(1). 42–63. 1 indexed citations
7.
Drton, Mathias & Michael D. Perlman. (2005). A SINful Approach to Gaussian Graphical Model Selection. arXiv (Cornell University). 7 indexed citations
8.
Perlman, Michael D. & Lang Wu. (2004). A Note on One‐Sided Tests with Multiple Endpoints. Biometrics. 60(1). 276–280. 19 indexed citations
9.
Wu, Lang & Michael D. Perlman. (2000). Efficiency of lattice conditional independence models for multinormal samples with non-monotone missing data. Communications in Statistics - Simulation and Computation. 29(2). 481–509. 1 indexed citations
10.
Andersson, Steen A. & Michael D. Perlman. (1998). Normal Linear Regression Models With Recursive Graphical Markov Structure. Journal of Multivariate Analysis. 66(2). 133–187. 19 indexed citations
11.
Madigan, David, Steen A. Andersson, Michael D. Perlman, & Chris Volinsky. (1996). Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs. Communication in Statistics- Theory and Methods. 25(11). 2493–2519. 78 indexed citations
12.
Andersson, Staffan & Michael D. Perlman. (1995). Unbiasedness of the Likelihood Ratio Test for Lattice Conditional Independence Models. Journal of Multivariate Analysis. 53(1). 1–17. 7 indexed citations
13.
Gleser, Leon Jay, Michael D. Perlman, S. James Press, & Allan R. Sampson. (1994). A brief biography and appreciation of Ingram Olkin. Linear Algebra and its Applications. 199. 1–15. 1 indexed citations
14.
Andersson, Steen A. & Michael D. Perlman. (1994). A characterization of matrix groups that act transitively on the cone of positive definite matrices. Linear Algebra and its Applications. 199. 151–170. 1 indexed citations
15.
Andersson, Steen A. & Michael D. Perlman. (1984). Two testing problems relating the real and complex multivariate normal distributions. Journal of Multivariate Analysis. 15(1). 21–51. 17 indexed citations
16.
Perlman, Michael D. & Michael J. Wichura. (1975). Sharpening Button's Needle. The American Statistician. 29(4). 157–163. 10 indexed citations
17.
Perlman, Michael D.. (1974). Jensen's inequality for a convex vector-valued function on an infinite-dimensional space. Journal of Multivariate Analysis. 4(1). 52–65. 47 indexed citations
18.
Perlman, Michael D.. (1974). On the monotonicity of the power functions of tests based on traces of multivariate beta matrices. Journal of Multivariate Analysis. 4(1). 22–30. 5 indexed citations
19.
Fefferman, Charles, Max Jodeit, & Michael D. Perlman. (1972). A Spherical Surface Measure Inequality for Convex Sets. Proceedings of the American Mathematical Society. 33(1). 114–114. 2 indexed citations
20.
Perlman, Michael D.. (1972). Characterizing measurability, distribution and weak convergence of random variables in a Banach space by total subsets of linear functionals. Journal of Multivariate Analysis. 2(2). 174–188. 7 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026