Antonio Punzo

2.0k total citations
93 papers, 1.1k citations indexed

About

Antonio Punzo is a scholar working on Artificial Intelligence, Statistics and Probability and Finance. According to data from OpenAlex, Antonio Punzo has authored 93 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Artificial Intelligence, 60 papers in Statistics and Probability and 20 papers in Finance. Recurrent topics in Antonio Punzo's work include Bayesian Methods and Mixture Models (59 papers), Statistical Methods and Bayesian Inference (33 papers) and Statistical Methods and Inference (26 papers). Antonio Punzo is often cited by papers focused on Bayesian Methods and Mixture Models (59 papers), Statistical Methods and Bayesian Inference (33 papers) and Statistical Methods and Inference (26 papers). Antonio Punzo collaborates with scholars based in Italy, United Kingdom and United States. Antonio Punzo's co-authors include Luca Bagnato, Antonello Maruotti, Angelo Mazza, Salvatore Ingrassia, Salvatore D. Tomarchio, Paul D. McNicholas, Simona C. Minotti, Brian E. McGuire, Giorgio Vittadini and Alessio Farcomeni and has published in prestigious journals such as The Journal of Experimental Medicine, Scientific Reports and Statistics in Medicine.

In The Last Decade

Antonio Punzo

85 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Antonio Punzo Italy 20 661 660 196 165 146 93 1.1k
Carlos M. Carvalho United States 13 402 0.6× 471 0.7× 149 0.8× 173 1.0× 81 0.6× 41 1.2k
Víctor H. Lachos Brazil 26 1.1k 1.7× 1.8k 2.8× 259 1.3× 141 0.9× 163 1.1× 134 2.3k
Marlene Müller Germany 10 206 0.3× 375 0.6× 97 0.5× 191 1.2× 66 0.5× 17 984
Yuan Liao United States 14 192 0.3× 696 1.1× 490 2.5× 713 4.3× 152 1.0× 48 1.7k
Deborah Nolan United States 15 401 0.6× 865 1.3× 118 0.6× 122 0.7× 144 1.0× 43 1.3k
Yong Zhou China 22 258 0.4× 1.1k 1.7× 158 0.8× 179 1.1× 120 0.8× 165 1.5k
Jin‐Ting Zhang Singapore 16 375 0.6× 766 1.2× 37 0.2× 60 0.4× 101 0.7× 71 1.2k
Chris Carter Australia 12 654 1.0× 504 0.8× 636 3.2× 831 5.0× 256 1.8× 29 2.2k
S. Ejaz Ahmed Canada 17 156 0.2× 785 1.2× 98 0.5× 122 0.7× 104 0.7× 159 1.1k
Yan Yu United States 15 264 0.4× 573 0.9× 270 1.4× 253 1.5× 93 0.6× 51 1.3k

Countries citing papers authored by Antonio Punzo

Since Specialization
Citations

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

Fields of papers citing papers by Antonio Punzo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Antonio Punzo

This figure shows the co-authorship network connecting the top 25 collaborators of Antonio Punzo. A scholar is included among the top collaborators of Antonio Punzo 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 Antonio Punzo. Antonio Punzo 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.
Punzo, Antonio, et al.. (2025). Heckman Selection-Contaminated Normal Model. Journal of Computational and Graphical Statistics. 1–13.
2.
Ferreira, Johan, et al.. (2024). A refreshing take on the inverted Dirichlet via a mode parameterization with some statistical illustrations. Journal of the Korean Statistical Society. 54(1). 314–341.
3.
Punzo, Antonio & Luca Bagnato. (2024). Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns. Advances in Data Analysis and Classification. 19(2). 275–322.
4.
Tomarchio, Salvatore D., Antonio Punzo, Johan Ferreira, & Andriëtte Bekker. (2024). A New Look at the Dirichlet Distribution: Robustness, Clustering, and Both Together. Journal of Classification. 42(1). 31–53. 4 indexed citations
5.
Bekker, Andriëtte, et al.. (2024). Uncovering a generalised gamma distribution: From shape to interpretation. Results in Applied Mathematics. 22. 100461–100461.
6.
Punzo, Antonio, et al.. (2024). Hidden semi-Markov models for rainfall-related insurance claims. Insurance Mathematics and Economics. 120. 91–106. 2 indexed citations
7.
Corrente, Salvatore, Carlo Ingrao, Antonio Punzo, & Agata Matarazzo. (2023). Evaluating citizens' satisfaction on the urban environmental management through a multi-criteria approach: An application experience in Sicily. Environmental Impact Assessment Review. 99. 107029–107029. 12 indexed citations
8.
Ingrassia, Salvatore, et al.. (2023). Local and Overall Deviance R-Squared Measures for Mixtures of Generalized Linear Models. Journal of Classification. 40(2). 233–266. 6 indexed citations
9.
Bagnato, Luca, Alessio Farcomeni, & Antonio Punzo. (2023). The generalized hyperbolic family and automatic model selection through the multiple‐choiceLASSO. Statistical Analysis and Data Mining The ASA Data Science Journal. 17(1). 2 indexed citations
10.
Browne, Ryan P., Luca Bagnato, & Antonio Punzo. (2023). Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions. Advances in Data Analysis and Classification. 18(3). 597–625. 1 indexed citations
11.
Tomarchio, Salvatore D., et al.. (2021). Mixtures of Matrix-Variate Contaminated Normal Distributions. Journal of Computational and Graphical Statistics. 31(2). 413–421. 16 indexed citations
12.
Farcomeni, Alessio, et al.. (2021). Assessing Measurement Invariance for Longitudinal Data through Latent Markov Models. Structural Equation Modeling A Multidisciplinary Journal. 29(3). 381–393. 3 indexed citations
13.
Punzo, Antonio & Luca Bagnato. (2021). Multiple scaled symmetric distributions in allometric studies. The International Journal of Biostatistics. 18(1). 219–242. 5 indexed citations
14.
Bagnato, Luca, et al.. (2020). Leptokurtic moment-parameterized elliptically contoured distributions with application to financial stock returns. Communication in Statistics- Theory and Methods. 51(2). 486–500. 1 indexed citations
15.
Bakk, Zsuzsa, et al.. (2019). A Random-covariate Approach for Distal Outcome Prediction with Latent Class Analysis. Structural Equation Modeling A Multidisciplinary Journal. 27(3). 351–368. 6 indexed citations
16.
Punzo, Antonio & Paul D. McNicholas. (2013). Robust Clustering via Parsimonious Mixtures of Contaminated Gaussian Distributions. arXiv (Cornell University). 2 indexed citations
17.
Punzo, Antonio, et al.. (2013). Rasch analysis for binary data with nonignorable nonresponses. Aisberg (University of Bergamo). 34(1). 97–123. 8 indexed citations
18.
Punzo, Antonio & Paul D. McNicholas. (2013). Outlier Detection via Parsimonious Mixtures of Contaminated Gaussian Distributions. arXiv (Cornell University). 5 indexed citations
19.
Ruggiero, C., et al.. (2012). Estimation of relative growth rate of ten field-grown herbaceous species: the effects of LAR and NAR depend on time scale and type of analysis.. 3(2). 57–63. 1 indexed citations
20.
Mazza, Angelo, et al.. (2012). BETA KERNEL GRADUATION OF MORTALITY DATA IN R. AN APPLICATION TO THE ENNA PROVINCE. 66. 15–22.

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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