Silvia Cagnone

Personal Details

First Name:	Silvia
Middle Name:
Last Name:	Cagnone
Suffix:
RePEc Short-ID:	pca1175
	[This author has chosen not to make the email address public]
	https://www.unibo.it/sitoweb/silvia.cagnone

Affiliation

Dipartimento di Scienze Statistiche "Paolo Fortunati"
Alma Mater Studiorum - Università di Bologna

Bologna, Italy
http://www.stat.unibo.it/
RePEc:edi:dsbolit (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

Sorry, no citations of working papers recorded.

Articles

1. Bianconcini, Silvia & Cagnone, Silvia, 2023. "The dimension-wise quadrature estimation of dynamic latent variable models for count data," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

   Cited by:

   1. Aghabazaz, Zeynab & Kazemi, Iraj, 2023. "Under-reported time-varying MINAR(1) process for modeling multivariate count series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).

2. Silvia Cagnone & 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, Royal Statistical Society, vol. 67(5), pages 1399-1418, November.

   Cited by:

   1. Schliep Erin M. & Schafer Toryn L. J. & Hawkey Matthew, 2021. "Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(3), pages 241-254, September.

3. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.

   Cited by:

   1. Prateek Bansal & Vahid Keshavarzzadeh & Angelo Guevara & Shanjun Li & Ricardo A Daziano, 2022. "Designed quadrature to approximate integrals in maximum simulated likelihood estimation [Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariat," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 301-321.
   2. Bianconcini, Silvia & Cagnone, Silvia, 2023. "The dimension-wise quadrature estimation of dynamic latent variable models for count data," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

4. Mazzocchi, Mario & Cagnone, Silvia & Bech-Larsen, Tino & Niedźwiedzka, Barbara & Saba, Anna & Shankar, Bhavani & Verbeke, Wim & Traill, W Bruce, 2015. "What is the public appetite for healthy eating policies? Evidence from a cross-European survey," Health Economics, Policy and Law, Cambridge University Press, vol. 10(3), pages 267-292, July.

   Cited by:

   1. Romain Cadario & Pierre Chandon, 2019. "Viewpoint: Effectiveness or consumer acceptance? Tradeoffs in selecting healthy eating nudges," Post-Print hal-02508983, HAL.
   2. Romain Espinosa & Anis Nassar, 2021. "The Acceptability of Food Policies," Post-Print halshs-03210654, HAL.
   3. Sara Fernández Sánchez-Escalonilla & Carlos Fernández-Escobar & Miguel Ángel Royo-Bordonada, 2022. "Public Support for the Imposition of a Tax on Sugar-Sweetened Beverages and the Determinants of Such Support in Spain," IJERPH, MDPI, vol. 19(7), pages 1-12, March.
   4. Cadario, Romain & Chandon, Pierre, 2019. "Viewpoint: Effectiveness or consumer acceptance? Tradeoffs in selecting healthy eating nudges," Food Policy, Elsevier, vol. 85(C), pages 1-6.
   5. Emily Lancsar & Jemimah Ride & Nicole Black & Leonie Burgess & Anna Peeters, 2022. "Social acceptability of standard and behavioral economic inspired policies designed to reduce and prevent obesity," Health Economics, John Wiley & Sons, Ltd., vol. 31(1), pages 197-214, January.
   6. Cristina Cavero Esponera & Sara Fernández Sánchez-Escalonilla & Miguel Ángel Royo-Bordonada, 2022. "Public Opinion on Food Policies to Combat Obesity in Spain," IJERPH, MDPI, vol. 19(14), pages 1-11, July.
   7. Lan Nguyen & Hans De Steur, 2021. "Public Acceptability of Policy Interventions to Reduce Sugary Drink Consumption in Urban Vietnam," Sustainability, MDPI, vol. 13(23), pages 1-18, December.
   8. Cesar Revoredo-Giha & Neil Chalmers & Faical Akaichi, 2018. "Simulating the Impact of Carbon Taxes on Greenhouse Gas Emission and Nutrition in the UK," Sustainability, MDPI, vol. 10(1), pages 1-19, January.
   9. Reynolds, J.P. & Archer, S. & Pilling, M. & Kenny, M. & Hollands, G.J. & Marteau, T.M., 2019. "Public acceptability of nudging and taxing to reduce consumption of alcohol, tobacco, and food: A population-based survey experiment," Social Science & Medicine, Elsevier, vol. 236(C), pages 1-1.
   10. Antonella Samoggia & Aldo Bertazzoli & Arianna Ruggeri, 2019. "European Rural Development Policy Approaching Health Issues: An Exploration of Programming Schemes," IJERPH, MDPI, vol. 16(16), pages 1-30, August.
   11. Molly Thomas-Meyer & Oliver Mytton & Jean Adams, 2017. "Public responses to proposals for a tax on sugar-sweetened beverages: A thematic analysis of online reader comments posted on major UK news websites," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-18, November.
   12. Reynolds, J.P. & Pilling, M. & Marteau, T.M., 2018. "Communicating quantitative evidence of policy effectiveness and support for the policy: Three experimental studies," Social Science & Medicine, Elsevier, vol. 218(C), pages 1-12.
   13. Hunter, Erik & Röös, Elin, 2016. "Fear of climate change consequences and predictors of intentions to alter meat consumption," Food Policy, Elsevier, vol. 62(C), pages 151-160.
   14. Revoredo-Giha, Cesar & Chalmers, Neil & Akaichi, Faical, 2018. "Measuring the trade-off between greenhouse gas emissions and nutrition due to carbon consumption taxes in the UK," 92nd Annual Conference, April 16-18, 2018, Warwick University, Coventry, UK 273481, Agricultural Economics Society.
   15. Martha Bicket & Robin Vanner, 2016. "Designing Policy Mixes for Resource Efficiency: The Role of Public Acceptability," Sustainability, MDPI, vol. 8(4), pages 1-17, April.
   16. Reisch, Lucia A. & Sunstein, Cass R. & Gwozdz, Wencke, 2017. "Viewpoint: Beyond carrots and sticks: Europeans support health nudges," Food Policy, Elsevier, vol. 69(C), pages 1-10.
   17. Mantzari, Eleni & Reynolds, James P. & Jebb, Susan A. & Hollands, Gareth J. & Pilling, Mark A. & Marteau, Theresa M., 2022. "Public support for policies to improve population and planetary health: A population-based online experiment assessing impact of communicating evidence of multiple versus single benefits," Social Science & Medicine, Elsevier, vol. 296(C).
   18. Cornelsen, Laura & Mazzocchi, Mario & Smith, Richard D., 2019. "Fat tax or thin subsidy? How price increases and decreases affect the energy and nutrient content of food and beverage purchases in Great Britain," Social Science & Medicine, Elsevier, vol. 230(C), pages 318-327.
   19. Van Loo, Ellen J. & Hoefkens, Christine & Verbeke, Wim, 2017. "Healthy, sustainable and plant-based eating: Perceived (mis)match and involvement-based consumer segments as targets for future policy," Food Policy, Elsevier, vol. 69(C), pages 46-57.
   20. Pettigrew, Simone & Booth, Leon & Farrar, Victoria & Brown, Julie & Karl, Charles & Godic, Branislava & Vidanaarachchi, Rajith & Thompson, Jason, 2024. "Public support for proposed government policies to optimise the social benefits of autonomous vehicles," Transport Policy, Elsevier, vol. 149(C), pages 264-270.
   21. Jessica Aschemann-Witzel & Tino Bech-Larsen & Sara Capacci, 2016. "Do Target Groups Appreciate Being Targeted? An Exploration of Healthy Eating Policy Acceptance," Journal of Consumer Policy, Springer, vol. 39(3), pages 285-306, September.
   22. Dragos C Petrescu & Gareth J Hollands & Dominique-Laurent Couturier & Yin-Lam Ng & Theresa M Marteau, 2016. "Public Acceptability in the UK and USA of Nudging to Reduce Obesity: The Example of Reducing Sugar-Sweetened Beverages Consumption," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-18, June.

5. Lucia Modugno & Silvia Cagnone & Simone Giannerini, 2015. "A multilevel model with autoregressive components for the analysis of tribal art prices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(10), pages 2141-2158, October.

   Cited by:

   1. Fikret Korhan Turan & Zeynep Tosun, 2023. "Sustainable development of art industry and a statistical analysis of the factors that influence the gallery prices of contemporary artworks," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(3), pages 1790-1804, June.
   2. Petrov, Nikita & Ratnikova, Tatiana, 2017. "The price index for the paintings of Henri Matisse: The sensitivity to the method of construction and connection with stock market and art indices," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 47, pages 49-73.

6. Silvia Cagnone & Cinzia Viroli, 2014. "A factor mixture model for analyzing heterogeneity and cognitive structure of dementia," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(1), pages 1-20, January.

   Cited by:

   1. Robin Fuchs & Denys Pommeret & Cinzia Viroli, 2022. "Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 31-53, March.
   2. Silvia Cagnone & 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, Royal Statistical Society, vol. 67(5), pages 1399-1418, November.
   3. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.

7. Silvia Cagnone & Paola Monari, 2013. "Latent variable models for ordinal data by using the adaptive quadrature approximation," Computational Statistics, Springer, vol. 28(2), pages 597-619, April.

   Cited by:

   1. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
   2. Marino, Maria Francesca & Alfó, Marco, 2016. "Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 193-209.
   3. Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.
   4. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.
   5. Bianconcini, Silvia & Cagnone, Silvia, 2012. "Estimation of generalized linear latent variable models via fully exponential Laplace approximation," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 183-193.
   6. Cagnone, Silvia & Bartolucci, Francesco, 2013. "Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data," MPRA Paper 51037, University Library of Munich, Germany.
   7. Andersson, Björn & Jin, Shaobo & Zhang, Maoxin, 2023. "Fast estimation of multiple group generalized linear latent variable models for categorical observed variables," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).

8. Bianconcini, Silvia & Cagnone, Silvia, 2012. "Estimation of generalized linear latent variable models via fully exponential Laplace approximation," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 183-193.

   Cited by:

   1. Kelava, Augustin & Kohler, Michael & Krzyżak, Adam & Schaffland, Tim Fabian, 2017. "Nonparametric estimation of a latent variable model," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 112-134.
   2. Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.
   3. Jaewoo Park & Sangwan Lee, 2022. "A projection‐based Laplace approximation for spatial latent variable models," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
   4. Jenni Niku & David I. Warton & Francis K. C. Hui & Sara Taskinen, 2017. "Generalized Linear Latent Variable Models for Multivariate Count and Biomass Data in Ecology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 498-522, December.
   5. Andersson, Björn & Jin, Shaobo & Zhang, Maoxin, 2023. "Fast estimation of multiple group generalized linear latent variable models for categorical observed variables," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).

9. Vassilis Vasdekis & Silvia Cagnone & Irini Moustaki, 2012. "A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 425-441, July.

   Cited by:

   1. Siliang Zhang & Yunxiao Chen, 2022. "Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1473-1502, December.
   2. Zhang, Siliang & Chen, Yunxiao, 2022. "Computation for latent variable model estimation: a unified stochastic proximal framework," LSE Research Online Documents on Economics 114489, London School of Economics and Political Science, LSE Library.
   3. Katsikatsou, Myrsini & Moustaki, Irini & Yang-Wallentin, Fan & Jöreskog, Karl G., 2012. "Pairwise likelihood estimation for factor analysis models with ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4243-4258.
   4. Papageorgiou, Ioulia & Moustaki, Irini, 2019. "Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables," LSE Research Online Documents on Economics 87592, London School of Economics and Political Science, LSE Library.
   5. K. Florios & I. Moustaki & D. Rizopoulos & V. Vasdekis, 2015. "A modified weighted pairwise likelihood estimator for a class of random effects models," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 217-228, August.
   6. Minjeong Jeon & Sophia Rabe-Hesketh, 2016. "An autoregressive growth model for longitudinal item analysis," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 830-850, September.
   7. Myrsini Katsikatsou & Irini Moustaki, 2016. "Pairwise Likelihood Ratio Tests and Model Selection Criteria for Structural Equation Models with Ordinal Variables," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1046-1068, December.

10. Silvia Bianconcini & Silvia Cagnone, 2012. "A General Multivariate Latent Growth Model With Applications to Student Achievement," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 339-364, April.

    Cited by:

    1. J. R. Lockwood & D. McCaffrey, 2020. "Using hidden information and performance level boundaries to study student–teacher assignments: implications for estimating teacher causal effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1333-1362, October.
    2. Chun Wang & Gongjun Xu & Xue Zhang, 2019. "Correction for Item Response Theory Latent Trait Measurement Error in Linear Mixed Effects Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 673-700, September.
    3. Anna Simonetto & Emma Zavarrone, 2015. "A micro approach to cognitive skills’ growth in a university context," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1013-1022, May.
    4. J. R. Lockwood & Daniel F. McCaffrey, 2014. "Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects," Journal of Educational and Behavioral Statistics, , vol. 39(1), pages 22-52, February.

11. Silvia Bianconcini & Silvia Cagnone & Stefania Mignani & paola.monari@unibo.it, 2007. "A latent curve analysis of unobserved heterogeneity in university achievements," Statistica, Department of Statistics, University of Bologna, vol. 67(1), pages 55-67.

    Cited by:

    1. Silvia Bianconcini & Silvia Cagnone, 2012. "A General Multivariate Latent Growth Model With Applications to Student Achievement," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 339-364, April.

12. Silvia cagnone & Stefania Mignani, 2007. "Assessing the goodness of fit of a latent variable model for ordinal data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 337-361.

    Cited by:

    1. Mark Reiser & Silvia Cagnone & Junfei Zhu, 2023. "An Extended GFfit Statistic Defined on Orthogonal Components of Pearson’s Chi-Square," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 208-240, March.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-ECM: Econometrics (2) 2013-11-09 2021-05-17
2. NEP-ORE: Operations Research (2) 2013-11-09 2021-05-17
3. NEP-ETS: Econometric Time Series (1) 2013-11-09

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