Silvia Cagnone

469 total citations
28 papers, 301 citations indexed

About

Silvia Cagnone is a scholar working on Statistics and Probability, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, Silvia Cagnone has authored 28 papers receiving a total of 301 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Statistics and Probability, 9 papers in Management Science and Operations Research and 7 papers in Artificial Intelligence. Recurrent topics in Silvia Cagnone's work include Statistical Methods and Bayesian Inference (10 papers), Statistical Methods and Inference (7 papers) and Advanced Statistical Methods and Models (6 papers). Silvia Cagnone is often cited by papers focused on Statistical Methods and Bayesian Inference (10 papers), Statistical Methods and Inference (7 papers) and Advanced Statistical Methods and Models (6 papers). Silvia Cagnone collaborates with scholars based in Italy, United Kingdom and Greece. Silvia Cagnone's co-authors include Cristina Bernini, Silvia Bianconcini, Vassilis Vasdekis, Irini Moustaki, Cinzia Viroli, W. Bruce Traill, Wim Verbeke, Anna Saba, Barbara Niedźwiedzka and Bhavani Shankar and has published in prestigious journals such as Statistics in Medicine, Psychometrika and Educational and Psychological Measurement.

In The Last Decade

Silvia Cagnone

27 papers receiving 286 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Silvia Cagnone Italy 8 98 65 57 53 38 28 301
Dolores López‐Montiel Spain 5 81 0.8× 16 0.2× 31 0.5× 28 0.5× 14 0.4× 15 354
Carmen Ximénez Spain 11 47 0.5× 14 0.2× 38 0.7× 16 0.3× 25 0.7× 34 362
Frank van de Pol Netherlands 6 162 1.7× 13 0.2× 91 1.6× 111 2.1× 8 0.2× 7 476
Terry C. Gleason United States 11 93 0.9× 7 0.1× 60 1.1× 25 0.5× 15 0.4× 18 412
Vern W. Urry United Kingdom 6 85 0.9× 21 0.3× 65 1.1× 20 0.4× 74 1.9× 7 479
Xin Tong United States 10 121 1.2× 16 0.2× 17 0.3× 77 1.5× 3 0.1× 40 384
Sanne C. Smid Netherlands 5 61 0.6× 14 0.2× 37 0.6× 27 0.5× 4 0.1× 8 224
Avi Allalouf United States 8 30 0.3× 20 0.3× 22 0.4× 15 0.3× 7 0.2× 14 318
Anne L. Harvey United States 11 43 0.4× 53 0.8× 28 0.5× 13 0.2× 9 0.2× 26 367
Douglas R. Whitney United States 11 28 0.3× 29 0.4× 26 0.5× 11 0.2× 10 0.3× 37 388

Countries citing papers authored by Silvia Cagnone

Since Specialization
Citations

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

Fields of papers citing papers by Silvia Cagnone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Silvia Cagnone

This figure shows the co-authorship network connecting the top 25 collaborators of Silvia Cagnone. A scholar is included among the top collaborators of Silvia Cagnone 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 Silvia Cagnone. Silvia Cagnone 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.
Reiser, Mark, et al.. (2022). An Extended GFfit Statistic Defined on Orthogonal Components of Pearson’s Chi-Square. Psychometrika. 88(1). 208–240. 2 indexed citations
2.
Bianconcini, Silvia & Silvia Cagnone. (2022). The dimension-wise quadrature estimation of dynamic latent variable models for count data. Computational Statistics & Data Analysis. 177. 107585–107585. 1 indexed citations
3.
Cagnone, Silvia, et al.. (2021). Use of the Lagrange Multiplier Test for Assessing Measurement Invariance Under Model Misspecification. Educational and Psychological Measurement. 82(2). 254–280. 3 indexed citations
4.
Cagnone, Silvia & Cinzia Viroli. (2018). Multivariate Latent Variable Transition Models of Longitudinal Mixed Data: An Analysis on Alcohol Use Disorder. Journal of the Royal Statistical Society Series C (Applied Statistics). 67(5). 1399–1418. 1 indexed citations
5.
Cagnone, Silvia & Francesco Bartolucci. (2016). Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models. Computational Economics. 49(4). 599–622. 4 indexed citations
6.
Cagnone, Silvia, et al.. (2015). A Power Study of the GFfit Statistic as a Lack-of-Fit Diagnostic. 3887–3899. 1 indexed citations
7.
Mazzocchi, Mario, Silvia Cagnone, Tino Bech‐Larsen, et al.. (2014). What is the public appetite for healthy eating policies? Evidence from a cross-European survey. Health Economics Policy and Law. 10(3). 267–292. 98 indexed citations
8.
Cagnone, Silvia & Cinzia Viroli. (2013). A factor mixture model for analyzing heterogeneity and cognitive structure of dementia. AStA Advances in Statistical Analysis. 98(1). 1–20. 3 indexed citations
9.
Cagnone, Silvia & Cinzia Viroli. (2012). A factor mixture analysis model for multivariate binary data. Statistical Modelling. 12(3). 257–277. 21 indexed citations
10.
Vasdekis, Vassilis, Silvia Cagnone, & Irini Moustaki. (2012). A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses. Psychometrika. 77(3). 425–441. 19 indexed citations
11.
Cagnone, Silvia. (2012). A Note on Goodness-of-Fit Test in Latent Variable Models with Categorical Variables. Communication in Statistics- Theory and Methods. 41(16-17). 2983–2990. 2 indexed citations
12.
Bianconcini, Silvia & Silvia Cagnone. (2012). Estimation of generalized linear latent variable models via fully exponential Laplace approximation. Journal of Multivariate Analysis. 112. 183–193. 17 indexed citations
13.
Bianconcini, Silvia & Silvia Cagnone. (2012). Multivariate Latent Growth Models for Mixed Data with Covariate Effects. Communication in Statistics- Theory and Methods. 41(16-17). 3079–3093. 3 indexed citations
14.
Cagnone, Silvia. (2009). Regressione lineare multipla 2.
15.
Cagnone, Silvia, Irini Moustaki, & Vassilis Vasdekis. (2008). Latent variable models for multivariate longitudinal ordinal responses. British Journal of Mathematical and Statistical Psychology. 62(2). 401–415. 26 indexed citations
16.
Cagnone, Silvia, et al.. (2008). University Formative Process : Quality of Teaching Versus Performance Indicators.. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 10(10). 191–203. 1 indexed citations
17.
Cagnone, Silvia, et al.. (2007). Assessing the goodness of fit of a latent variable model for ordinal data. METRON. 337–361. 4 indexed citations
18.
Cagnone, Silvia & Roberto Ricci. (2005). Student ability assessment based on two IRT models. Repository of the University of Ljubljana (University of Ljubljana). 2(2). 209–218. 2 indexed citations
19.
Cagnone, Silvia & Roberto Ricci. (2005). STUDENT ABILITY ASSESSMENT ON TWO IRT MODELS. 2. 209–218. 3 indexed citations
20.
Cagnone, Silvia, et al.. (2004). A comparison among different solutions for assessing the goodness of fit of a generalized linear latent variable model for ordinal data. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 16. 1–19. 3 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

Breakdown of academic impact, for papers by Dolores López‐Montiel Breakdown of academic impact, for papers by Carmen Ximénez Breakdown of academic impact, for papers by Frank van de Pol Breakdown of academic impact, for papers by Terry C. Gleason Breakdown of academic impact, for papers by Vern W. Urry Breakdown of academic impact, for papers by Xin Tong Breakdown of academic impact, for papers by Sanne C. Smid Breakdown of academic impact, for papers by Avi Allalouf Breakdown of academic impact, for papers by Anne L. Harvey Breakdown of academic impact, for papers by Douglas R. Whitney
Rankless by CCL
2026