Domenico Piccolo

Personal Details

First Name:	Domenico
Middle Name:
Last Name:	Piccolo
Suffix:
RePEc Short-ID:	ppi354
	https://www.docenti.unina.it/DOMENICO.PICCOLO

Affiliation

Università degli Studi di Napoli Federico II, Dipartimento di Scienze Politiche (University of Naples Federico II, Department of Political Sciences)

http://scienzepolitiche.dip.unina.it/
Napoli (ITALY)

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

1. Stefania Capecchi & Maria Iannario & Domenico Piccolo, 2012. "Modelling Job Satisfaction in AlmaLaurea Surveys," Working Papers 56, AlmaLaurea Inter-University Consortium.

   Cited by:

   1. Claudio Ceccarelli & Stefano De Santis & Francesca Petrarca, 2018. "The work path of Italian graduates using integration among different sources," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 72(4), pages 89-100, October-D.
   2. Andrea Cammelli & Francesco Ferrante, 2014. "Entrepreneurship and innovation: the role played by graduates," Working Papers 69, AlmaLaurea Inter-University Consortium.
   3. Stefania Capecchi & Domenico Piccolo, 2016. "Investigating the determinants of job satisfaction of Italian graduates: a model-based approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 169-179, January.
   4. Claudio Ceccarelli & Silvia Montagna & Francesca Petrarca, 2017. "Longitudinal Analysis Of Italian Graduates Employment Paths Through Integration Among Different Administrative Sources," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 71(3), pages 2-10, July-Sept.

2. Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2009. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fairtrade Coffee," 2009 International European Forum, February 15-20, 2009, Innsbruck-Igls, Austria 59209, International European Forum on System Dynamics and Innovation in Food Networks.
   • Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2010. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fair Trade Coffee," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 1(1), pages 1-12.

   Cited by:

   1. Gaëlle BALINEAU, 2017. "Fair Trade? Yes, but not at Christmas! Evidence from scanner data on real French Fairtrade purchases," Working Paper ab9a0fd1-6ad5-441b-879b-3, Agence française de développement.
   2. Marcella Corduas & Alfonso Piscitelli, 2017. "Modeling university student satisfaction: the case of the humanities and social studies degree programs," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 617-628, March.
   3. Van Loo, Ellen J. & Caputo, Vincenzina & Nayga, Rodolfo M. & Seo, Han-Seok & Zhang, Baoyue & Verbeke, Wim, 2015. "Sustainability labels on coffee: Consumer preferences, willingness-to-pay and visual attention to attributes," Ecological Economics, Elsevier, vol. 118(C), pages 215-225.
   4. Volker Lingnau & Florian Fuchs & Florian Beham, 2019. "The impact of sustainability in coffee production on consumers’ willingness to pay–new evidence from the field of ethical consumption," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 30(1), pages 65-93, April.
   5. Corduas, Marcella, 2015. "A statistical model for consumer preferences: the case of Italian extra virgin olive oil," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202701, European Association of Agricultural Economists.
   6. Hellberg-Bahr, Anneke & Pfeuffer, Martin & Spiller, Achim & Brümmer, Bernhard, 2011. "Using Price Rigidities to Explain Pricing Strategies in the Organic Milk Chain," 2011 International European Forum, February 14-18, 2011, Innsbruck-Igls, Austria 122003, International European Forum on System Dynamics and Innovation in Food Networks.
   7. Arboretti Giancristofaro, Rosa & Bordignon, Paolo, 2015. "Consumer preferences in food packaging: cub models and conjoint analysis," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202707, European Association of Agricultural Economists.
   8. Fitzsimmons, Jill & Cicia, Gianni, 2018. "Different Tubers for Different Consumers: Heterogeneity in Human Values and Willingness to Pay for Social Outcomes of Potato Credence Attributes," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 9(4), August.
   9. Takahashi, R. & Todo, Y., 2018. "When do consumers stand up for the environment? Evidence from a large-scale social experiment to promote environmentally friendly coffee," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277507, International Association of Agricultural Economists.
   10. Takahashi, Ryo & Todo, Yasuyuki & Funaki, Yukihiko, 2018. "How Can We Motivate Consumers to Purchase Certified Forest Coffee? Evidence From a Laboratory Randomized Experiment Using Eye-trackers," Ecological Economics, Elsevier, vol. 150(C), pages 107-121.
   11. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
   12. Rotaris Lucia & Danielis Romeo, 2011. "Willingness to Pay for Fair Trade Coffee: A Conjoint Analysis Experiment with Italian Consumers," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 9(1), pages 1-22, June.
   13. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," 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. 6(4), pages 303-321, December.
   14. Francesca Colantuoni & Gianni Cicia & Teresa Del Giudice & Daniel Lass & Francesco Caracciolo & Pasquale Lombardi, 2016. "Heterogeneous Preferences for Domestic Fresh Produce: Evidence from German and Italian Early Potato Markets," Agribusiness, John Wiley & Sons, Ltd., vol. 32(4), pages 512-530, November.
   15. Panico, Teresa & Verneau, Fabio & Capone, Vincenza & La Barbera, Francesco & Del Giudice, Teresa, 2017. "Antecedents of Intention and Behavior Towards Fair Trade Products: A Study on Values and Attitudes in Italy," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 8(2), March.

Articles

1. Leonardo Grilli & Maria Iannario & Domenico Piccolo & Carla Rampichini, 2014. "Latent class CUB models," 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. 8(1), pages 105-119, March.

   Cited by:

   1. Manisera, Marica & Zuccolotto, Paola, 2015. "Identifiability of a model for discrete frequency distributions with a multidimensional parameter space," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 302-316.
   2. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
   3. Anna Gottard & Maria Iannario & Domenico Piccolo, 2016. "Varying uncertainty in CUB models," 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. 10(2), pages 225-244, June.
   4. Maurizio Carpita & Enrico Ciavolino & Mariangela Nitti, 2019. "The MIMIC–CUB Model for the Prediction of the Economic Public Opinions in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 287-305, November.
   5. Domenico Piccolo & Rosaria Simone, 2019. "Rejoinder to the discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 477-493, September.
   6. Gerhard Tutz & Micha Schneider & Maria Iannario & Domenico Piccolo, 2017. "Mixture models for ordinal responses to account for uncertainty of choice," 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. 11(2), pages 281-305, June.
   7. Manisera, Marica & Zuccolotto, Paola, 2014. "Modeling rating data with Nonlinear CUB models," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 100-118.
   8. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2020. "Ordinal Data Models for No-Opinion Responses in Attitude Survey," Sociological Methods & Research, , vol. 49(1), pages 250-276, February.

2. Piccolo, Domenico & Capecchi, Stefania & Iannario, Maria & Corduas, Marcella, 2013. "Modelling Consumer Preferences For Extra Virgin Olive Oil: The Italian Case," Politica Agricola Internazionale - International Agricultural Policy, Edizioni L'Informatore Agrario, vol. 2013(1), March.

   Cited by:

   1. Lucio Cappelli & Fabrizio D’Ascenzo & Maria Felice Arezzo & Roberto Ruggieri & Irina Gorelova, 2020. "The Willingness to Pay in the Food Sector. Testing the Hypothesis of Consumer Preferences for Some Made in Italy Products," Sustainability, MDPI, vol. 12(15), pages 1-11, August.
   2. Barbara Cafarelli & Piermichele La Sala & Giustina Pellegrini & Mariantonietta Fiore, 2017. "Consumers? preferences investigation for extra virgin olive oil basing on conjoint analysis," RIVISTA DI STUDI SULLA SOSTENIBILITA', FrancoAngeli Editore, vol. 0(1), pages 203-218.
   3. Yamna Erraach & Fatma Jaafer & Ivana Radić & Mechthild Donner, 2021. "Sustainability Labels on Olive Oil: A Review on Consumer Attitudes and Behavior," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
   4. Teresa Del Giudice & Carla Cavallo & Francesco Caracciolo & Gianni Cicia, 2015. "What attributes of extra virgin olive oil are really important for consumers: a meta-analysis of consumers’ stated preferences," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 3(1), pages 1-15, December.

3. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," 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. 6(4), pages 303-321, December.

   Cited by:

   1. Manisera, Marica & Zuccolotto, Paola, 2015. "Identifiability of a model for discrete frequency distributions with a multidimensional parameter space," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 302-316.
   2. Corduas, Marcella, 2015. "A statistical model for consumer preferences: the case of Italian extra virgin olive oil," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202701, European Association of Agricultural Economists.
   3. Amalia Vanacore & Maria Sole Pellegrino, 2019. "Checking quality of sensory data via an agreement-based approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2545-2556, September.
   4. Yanyu Zhang & Pafe Momoisea & Qixin Lin & Jiaqi Liang & Keegan Burrow & Luca Serventi, 2023. "Evaluation of Sensory and Physicochemical Characteristics of Vitamin B 12 Enriched Whole-Meal Sourdough Bread Fermented with Propionibacterium freudenreichii," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
   5. Jan Małecki & Konrad Terpiłowski & Maciej Nastaj & Bartosz G. Sołowiej, 2022. "Physicochemical, Nutritional, Microstructural, Surface and Sensory Properties of a Model High-Protein Bars Intended for Athletes Depending on the Type of Protein and Syrup Used," IJERPH, MDPI, vol. 19(7), pages 1-15, March.
   6. Manisera, Marica & Zuccolotto, Paola, 2014. "Modeling rating data with Nonlinear CUB models," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 100-118.
   7. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2020. "Ordinal Data Models for No-Opinion Responses in Attitude Survey," Sociological Methods & Research, , vol. 49(1), pages 250-276, February.

4. Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2010. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fair Trade Coffee," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 1(1), pages 1-12.
   • Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2009. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fairtrade Coffee," 2009 International European Forum, February 15-20, 2009, Innsbruck-Igls, Austria 59209, International European Forum on System Dynamics and Innovation in Food Networks.
   See citations under working paper version above.
5. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.

   Cited by:

   1. Francesca Di Iorio & Umberto Triacca, 2022. "A comparison between VAR processes jointly modeling GDP and Unemployment rate in France and Germany," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 617-635, September.
   2. Steinmann, Patrick & Auping, Willem L. & Kwakkel, Jan H., 2020. "Behavior-based scenario discovery using time series clustering," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
   3. Giulio Palomba & Emma Sarno & Alberto Zazzaro, 2009. "Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro," Empirical Economics, Springer, vol. 37(2), pages 231-270, October.
   4. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
   5. Umberto Triacca, 2016. "Measuring the Distance between Sets of ARMA Models," Econometrics, MDPI, vol. 4(3), pages 1-11, July.
   6. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
   7. Francesca Di Iorio & Umberto Triacca, 2014. "Testing for A Set of Linear Restrictions in VARMA Models Using Autoregressive Metric: An Application to Granger Causality Test," Econometrics, MDPI, vol. 2(4), pages 1-14, December.
   8. Alessandro De Gregorio & Stefano Iacus, 2008. "Clustering of discretely observed diffusion processes," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1077, Universitá degli Studi di Milano.
      • De Gregorio, Alessandro & Maria Iacus, Stefano, 2010. "Clustering of discretely observed diffusion processes," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 598-606, February.
      • Alessandro De Gregorio & Stefano Maria Iacus, 2008. "Clustering of discretely observed diffusion processes," Papers 0809.3902, arXiv.org.
   9. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
   10. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2014. "Clustering of financial time series in risky scenarios," 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. 8(4), pages 359-376, December.
   11. Anthony Bagnall & Gareth Janacek, 2014. "A Run Length Transformation for Discriminating Between Auto Regressive Time Series," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 154-178, July.
   12. Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020. "Machine Learning Fund Categorizations," Papers 2006.00123, arXiv.org.
   13. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
       • E. Otranto, 2008. "Identifying Financial Time Series with Similar Dynamic Conditional Correlation," Working Paper CRENoS 200817, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
   14. Di Iorio, Francesca & Triacca, Umberto, 2013. "Testing for Granger non-causality using the autoregressive metric," Economic Modelling, Elsevier, vol. 33(C), pages 120-125.
   15. Bob Walrave, 2016. "Determining intervention thresholds that change output behavior patterns," System Dynamics Review, System Dynamics Society, vol. 32(3-4), pages 261-278, July.
   16. Ozan Cinar & Ozlem Ilk & Cem Iyigun, 2018. "Clustering of short time-course gene expression data with dissimilar replicates," Annals of Operations Research, Springer, vol. 263(1), pages 405-428, April.
   17. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
   18. Pacifico, Antonio, 2020. "Bayesian Fuzzy Clustering with Robust Weighted Distance for Multiple ARIMA and Multivariate Time-Series," MPRA Paper 104379, University Library of Munich, Germany.
   19. Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A robust method for clustering football players with mixed attributes," Annals of Operations Research, Springer, vol. 325(1), pages 9-36, June.
   20. Paloma Taltavull de La Paz, 2021. "Predicting housing prices. A long term housing price path for Spanish regions," LARES lares-2021-4dra, Latin American Real Estate Society (LARES).
   21. Vilar, J.A. & Alonso, A.M. & Vilar, J.M., 2010. "Non-linear time series clustering based on non-parametric forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2850-2865, November.
   22. Leijiao Ge & Tianshuo Du & Changlu Li & Yuanliang Li & Jun Yan & Muhammad Umer Rafiq, 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications," Energies, MDPI, vol. 15(23), pages 1-24, November.
   23. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
   24. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
   25. E. Otranto, 2008. "Clustering Heteroskedastic Time Series by Model-Based Procedures," Working Paper CRENoS 200801, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
       • Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
   26. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
   27. Jane L. Harvill & Priya Kohli & Nalini Ravishanker, 2017. "Clustering Nonlinear, Nonstationary Time Series Using BSLEX," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 935-955, September.
   28. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," 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. 5(4), pages 323-340, December.
   29. De Luca Giovanni & Zuccolotto Paola, 2017. "A double clustering algorithm for financial time series based on extreme events," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 1-12, June.
   30. E. Otranto, 2011. "Classification of Volatility in Presence of Changes in Model Parameters," Working Paper CRENoS 201113, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
   31. Domenico Piccolo, 2012. "Discussion of “An analysis of global warming in the Alpine region based of nonlinear nonstationary time series models” by F. Battaglia and M. K. Protopapas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 363-369, August.

6. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.

   Cited by:

   1. Manisera, Marica & Zuccolotto, Paola, 2015. "Identifiability of a model for discrete frequency distributions with a multidimensional parameter space," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 302-316.
   2. Maria Iannario, 2012. "Preliminary estimators for a mixture model of ordinal data," 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. 6(3), pages 163-184, October.
   3. Donata Marasini & Piero Quatto & Enrico Ripamonti, 2016. "Intuitionistic fuzzy sets in questionnaire analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 767-790, March.
   4. Maria Iannario, 2010. "On the identifiability of a mixture model for ordinal data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 87-94.
   5. Ribecco, Nunziata & D'Uggento, Angela Maria & Labarile, Angela, 2022. "What influences the perception of immigration in Italian adolescents? An analysis with CUB models for rating data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
   6. M. Meleddu & M. Pulina & G. Solinas & S. Capecchi, 2019. "Mixture models for consumers' preferences in healthcare," Working Paper CRENoS 201901, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
   7. Rosaria Simone, 2021. "An accelerated EM algorithm for mixture models with uncertainty for rating data," Computational Statistics, Springer, vol. 36(1), pages 691-714, March.
   8. Gennaro Punzo & Rosalia Castellano & Mirko Buonocore, 2018. "Job Satisfaction in the “Big Four” of Europe: Reasoning Between Feeling and Uncertainty Through CUB Models," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(1), pages 205-236, August.
   9. Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2010. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fair Trade Coffee," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 1(1), pages 1-12.
      • Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2009. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fairtrade Coffee," 2009 International European Forum, February 15-20, 2009, Innsbruck-Igls, Austria 59209, International European Forum on System Dynamics and Innovation in Food Networks.
   10. Romina Gambacorta & Maria Iannario, 2012. "Statistical models for measuring job satisfaction," Temi di discussione (Economic working papers) 852, Bank of Italy, Economic Research and International Relations Area.
   11. Capecchi, Stefania & Amato, Mario & Sodano, Valeria & Verneau, Fabio, 2019. "Understanding beliefs and concerns towards palm oil: Empirical evidence and policy implications," Food Policy, Elsevier, vol. 89(C).
   12. Shaoting Li & Jiahua Chen, 2023. "Mixture of shifted binomial distributions for rating data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(5), pages 833-853, October.
   13. Simone, Rosaria & Tutz, Gerhard & Iannario, Maria, 2020. "Subjective heterogeneity in response attitude for multivariate ordinal outcomes," Econometrics and Statistics, Elsevier, vol. 14(C), pages 145-158.
   14. Stefania Capecchi & Maria Iannario, 2016. "Gini heterogeneity index for detecting uncertainty in ordinal data surveys," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 223-232, August.
   15. Maria Iannario, 2012. "Modelling shelter choices in a class of mixture models for ordinal responses," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 1-22, March.
   16. Stefania Capecchi & Maria Iannario & Domenico Piccolo, 2012. "Modelling Job Satisfaction in AlmaLaurea Surveys," Working Papers 56, AlmaLaurea Inter-University Consortium.
   17. Arboretti Giancristofaro, Rosa & Bordignon, Paolo, 2015. "Consumer preferences in food packaging: cub models and conjoint analysis," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202707, European Association of Agricultural Economists.
   18. Stefania Capecchi & Maria Iannario & Rosaria Simone, 2018. "Well-Being and Relational Goods: A Model-Based Approach to Detect Significant Relationships," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 729-750, January.
   19. Maurizio Carpita & Enrico Ciavolino & Mariangela Nitti, 2019. "The MIMIC–CUB Model for the Prediction of the Economic Public Opinions in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 287-305, November.
   20. Domenico Piccolo & Rosaria Simone, 2019. "Rejoinder to the discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 477-493, September.
   21. Donata Marasini & Piero Quatto & Enrico Ripamonti, 2017. "Inferential confidence intervals for fuzzy analysis of teaching satisfaction," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(4), pages 1513-1529, July.
   22. Leonardo Grilli & Maria Iannario & Domenico Piccolo & Carla Rampichini, 2014. "Latent class CUB models," 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. 8(1), pages 105-119, March.
   23. Ekhine Irurozki & Borja Calvo & Jose A. Lozano, 2018. "Sampling and Learning Mallows and Generalized Mallows Models Under the Cayley Distance," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 1-35, March.
   24. Gore, Madison & Joshi, Omkar & Chapagain, Binod & Poudyal, Neelam C. & Fairbanks, Sue, 2023. "Visitor satisfaction with WMAs: A case study from Oklahoma," Forest Policy and Economics, Elsevier, vol. 147(C).
   25. E. Nardo & R. Simone, 2019. "A model-based fuzzy analysis of questionnaires," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 187-215, June.
   26. Jyh-Shyang Wu & Wen-Shuenn Deng, 2017. "A nonparametric procedure for testing partially ranked data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 213-230, April.
   27. Stefania Capecchi & Domenico Piccolo, 2017. "Dealing with heterogeneity in ordinal responses," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2375-2393, September.
   28. Maria Iannario & Anna Clara Monti, 2022. "Modelling consumer perceptions of service quality for urban public transport systems using statistical models for ordinal data," METRON, Springer;Sapienza Università di Roma, vol. 80(1), pages 61-76, April.
   29. Stefania Capecchi & Rosaria Simone, 2019. "A Proposal for a Model-Based Composite Indicator: Experience on Perceived Discrimination in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 95-110, January.
   30. Jacques, Julien & Biernacki, Christophe, 2018. "Model-based co-clustering for ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 101-115.
   31. Roberto Colombi & Sabrina Giordano & Gerhard Tutz, 2021. "A Rating Scale Mixture Model to Account for the Tendency to Middle and Extreme Categories," Journal of Educational and Behavioral Statistics, , vol. 46(6), pages 682-716, December.
   32. Gerhard Tutz & Micha Schneider & Maria Iannario & Domenico Piccolo, 2017. "Mixture models for ordinal responses to account for uncertainty of choice," 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. 11(2), pages 281-305, June.
   33. Manisera, Marica & Zuccolotto, Paola, 2014. "Modeling rating data with Nonlinear CUB models," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 100-118.
   34. Stefania Capecchi & Marta Meleddu & Manuela Pulina, 2019. "Quality evaluation and preferences of healthcare services: the case of telemedicine in Sardinia," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2339-2351, September.
   35. Maria Iannario & Anna Clara Monti & Domenico Piccolo, 2016. "Robustness issues for cub models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(4), pages 731-750, December.
   36. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," 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. 6(4), pages 303-321, December.
   37. Maria Iannario & Marica Manisera & Paola Zuccolotto, 2017. "Treatment of “don’t know” responses in the consumers’ perceptions about sustainability in the agri-food sector," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 765-778, March.
   38. Manisera, Marica & Zuccolotto, Paola, 2022. "A mixture model for ordinal variables measured on semantic differential scales," Econometrics and Statistics, Elsevier, vol. 22(C), pages 98-123.
   39. Sasanka Adikari & Norou Diawara, 2024. "Utility in Time Description in Priority Best–Worst Discrete Choice Models: An Empirical Evaluation Using Flynn’s Data," Stats, MDPI, vol. 7(1), pages 1-18, February.
   40. Maria Iannario & Domenico Piccolo, 2016. "A comprehensive framework of regression models for ordinal data," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 233-252, August.
   41. Iannario, Maria & Piccolo, Domenico, 2014. "A theorem on CUB models for rank data," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 27-31.

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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-AGR: Agricultural Economics (1) 2010-05-22
2. NEP-HAP: Economics of Happiness (1) 2014-06-28

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