Mario Peruggia

713 total citations
36 papers, 512 citations indexed

About

Mario Peruggia is a scholar working on Statistics and Probability, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Mario Peruggia has authored 36 papers receiving a total of 512 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Statistics and Probability, 10 papers in Artificial Intelligence and 5 papers in Cognitive Neuroscience. Recurrent topics in Mario Peruggia's work include Statistical Methods and Bayesian Inference (10 papers), Bayesian Methods and Mixture Models (8 papers) and Statistical Methods and Inference (8 papers). Mario Peruggia is often cited by papers focused on Statistical Methods and Bayesian Inference (10 papers), Bayesian Methods and Mixture Models (8 papers) and Statistical Methods and Inference (8 papers). Mario Peruggia collaborates with scholars based in United States, Nepal and Switzerland. Mario Peruggia's co-authors include Jason C. Hsu, Steven N. MacEachern, Trisha Van Zandt, Peter F. Craigmile, Jason C. Hsu, Paul M. Suratt, Vito A. Perriello, Jeffrey T. Barth, Michael L. Johnson and Thomas J. Santner and has published in prestigious journals such as Journal of the American Statistical Association, PEDIATRICS and Endocrinology.

In The Last Decade

Mario Peruggia

34 papers receiving 479 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mario Peruggia United States 11 81 72 56 52 44 36 512
Pierre Lafaye de Micheaux France 16 231 2.9× 83 1.2× 44 0.8× 81 1.6× 2 0.0× 45 702
Damjan Krstajić United States 5 22 0.3× 110 1.5× 10 0.2× 46 0.9× 3 0.1× 8 786
Gangmin Ning China 12 16 0.2× 194 2.7× 12 0.2× 84 1.6× 12 0.3× 62 725
Simon Thomas United Kingdom 7 20 0.2× 107 1.5× 9 0.2× 85 1.6× 3 0.1× 8 934
Connor J. Dalzell Canada 4 290 3.6× 125 1.7× 42 0.8× 30 0.6× 2 0.0× 5 629
Graciela Estévez‐Pérez Spain 12 80 1.0× 41 0.6× 10 0.2× 22 0.4× 2 0.0× 21 555
Raquel Prado United States 12 111 1.4× 147 2.0× 57 1.0× 87 1.7× 2 0.0× 38 546
Matthew Reimherr United States 17 465 5.7× 199 2.8× 71 1.3× 35 0.7× 2 0.0× 47 1.1k
Nikolay Bliznyuk United States 12 25 0.3× 47 0.7× 18 0.3× 19 0.4× 4 0.1× 40 559

Countries citing papers authored by Mario Peruggia

Since Specialization
Citations

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

Fields of papers citing papers by Mario Peruggia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mario Peruggia. 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 Mario Peruggia. The network helps show where Mario Peruggia may publish in the future.

Co-authorship network of co-authors of Mario Peruggia

This figure shows the co-authorship network connecting the top 25 collaborators of Mario Peruggia. A scholar is included among the top collaborators of Mario Peruggia 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 Mario Peruggia. Mario Peruggia 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.
MacEachern, Steven N., et al.. (2023). Asymptotics of lower dimensional zero-density regions. Statistics. 57(6). 1285–1316. 1 indexed citations
2.
Peruggia, Mario, et al.. (2021). Empirical Bayes Model Averaging with Influential Observations: Tuning Zellner’s g Prior for Predictive Robustness. Econometrics and Statistics. 27. 102–119. 2 indexed citations
3.
Potter, Kevin, et al.. (2019). A Bayesian race model for response times under cyclic stimulus discriminability. The Annals of Applied Statistics. 13(1). 3 indexed citations
4.
Thomas, Zachary M., Steven N. MacEachern, & Mario Peruggia. (2017). Reconciling Curvature and Importance Sampling Based Procedures for Summarizing Case Influence in Bayesian Models. Journal of the American Statistical Association. 113(524). 1669–1683. 2 indexed citations
5.
Houpt, Joseph W., Steven N. MacEachern, Mario Peruggia, James T. Townsend, & Trisha Van Zandt. (2016). Semiparametric Bayesian approaches to systems factorial technology. Journal of Mathematical Psychology. 75. 68–85. 9 indexed citations
6.
Craigmile, Peter F., Mario Peruggia, & Trisha Van Zandt. (2013). A Bayesian hierarchical model for response time data providing evidence for criteria changes over time. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 2 indexed citations
7.
Yu, Qingzhao, Steven N. MacEachern, & Mario Peruggia. (2013). Clustered Bayesian Model Averaging. Bayesian Analysis. 8(4). 6 indexed citations
8.
Huang, Yifan, et al.. (2006). Statistical Selection of Maintenance Genes for Normalization of Gene Expressions. Statistical Applications in Genetics and Molecular Biology. 5(1). Article4–Article4. 4 indexed citations
9.
Peruggia, Mario. (2004). The Analysis of Time Series: An Introduction (6th ed.) (Book). 99(467). 906–907. 1 indexed citations
10.
Peruggia, Mario, et al.. (2004). Detecting stage-wise outliers in hierarchical Bayesian linear models of repeated measures data. Annals of the Institute of Statistical Mathematics. 56(3). 415–433. 2 indexed citations
11.
Peruggia, Mario. (2003). Total Least Squares and Errors-in-Variables Modeling: Analysis, Algorithms and Applications. 98(461). 260. 72 indexed citations
12.
Peruggia, Mario. (2003). Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data. 98(461). 259. 145 indexed citations
13.
Gray, Mikel, et al.. (2001). A Model for Predicting Motor Urge Urinary Incontinence. Nursing Research. 50(2). 116–122. 7 indexed citations
14.
MacEachern, Steven N. & Mario Peruggia. (2000). Importance Link Function Estimation for Markov Chain Monte Carlo Methods. Journal of Computational and Graphical Statistics. 9(1). 99–121. 13 indexed citations
15.
Nass, Ralf, Stacey M. Anderson, Bruce D. Gaylinn, et al.. (2000). High Plasma Growth Hormone (GH) Levels Inhibit Expression of GH Secretagogue Receptor Messenger Ribonucleic Acid Levels in the Rat Pituitary*. Endocrinology. 141(6). 2084–2089. 23 indexed citations
16.
Peruggia, Mario. (1997). On the Variability of Case-Deletion Importance Sampling Weights in the Bayesian Linear Model. Journal of the American Statistical Association. 92(437). 199–207. 26 indexed citations
17.
Goel, Prem K., et al.. (1997). Computer-Aided Teaching of Probabilistic Modeling for Biological Phenomena. The American Statistician. 51(2). 164–169. 1 indexed citations
18.
Peruggia, Mario. (1997). On the Variability of Case-Deletion Importance Sampling Weights in the Bayesian Linear Model. Journal of the American Statistical Association. 92(437). 199–199. 7 indexed citations
19.
Hsu, Jason C. & Mario Peruggia. (1994). Graphical Representations of Tukey's Multiple Comparison Method. Journal of Computational and Graphical Statistics. 3(2). 143–161. 31 indexed citations
20.
Hsu, Jason C. & Mario Peruggia. (1994). Graphical Representations of Tukey's Multiple Comparison Method. Journal of Computational and Graphical Statistics. 3(2). 143–143. 10 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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