IDEAS home Printed from https://ideas.repec.org/f/ppe590.html
   My authors  Follow this author

Fulvia Pennoni

Personal Details

First Name:Fulvia
Middle Name:
Last Name:Pennoni
Suffix:
RePEc Short-ID:ppe590
[This author has chosen not to make the email address public]
https://sites.google.com/view/fulviapennoni/home

Affiliation

Dipartimento di Statistica e Metodi Quantitativi (DISMEQ)
Università degli Studi di Milano-Bicocca

Milano, Italy
https://www.dismeq.unimib.it/
RePEc:edi:dsmibit (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Pennoni, Fulvia & Bartolucci, Francesco & Forte, Gianfranco & Ametrano, Ferdinando, 2020. "Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model," MPRA Paper 106150, University Library of Munich, Germany.
  2. Nakai, Miki & Pennoni, Fulvia, 2018. "A latent class analysis towards stability and changes in breadwinning patterns among coupled households," MPRA Paper 89950, University Library of Munich, Germany.
  3. Garriga, Anna & Pennoni, Fulvia, 2017. "The influence of parental divorce, parental temporary separation and parental relationship quality on children’s school readiness," MPRA Paper 82892, University Library of Munich, Germany.
  4. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.
  5. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
  6. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
  7. Pennoni, Fulvia & Tarantola, Stefano & Latvala, Ari, 2006. "The 2005 European e-Business Readiness Index," MPRA Paper 18048, University Library of Munich, Germany.

Articles

  1. Fulvia Pennoni & Francesco Bartolucci & Gianfranco Forte & Ferdinando Ametrano, 2022. "Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(1), February.
  2. Bassi, Francesca & Pennoni, Fulvia & Rossetto, Luca, 2021. "Market Segmentation and Dynamic Analysis of Sparkling Wine Purchases in Italy," Journal of Wine Economics, Cambridge University Press, vol. 16(3), pages 283-304, August.
  3. Marco Gemma & Fulvia Pennoni & Roberta Tritto & Massimo Agostoni, 2021. "Risk of adverse events in gastrointestinal endoscopy: Zero-inflated Poisson regression mixture model for count data and multinomial logit model for the type of event," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
  4. Francesca Bassi & Fulvia Pennoni & Luca Rossetto, 2020. "The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 542-567, October.
  5. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
  6. Pennoni Fulvia & Nakai Miki, 2019. "A latent class analysis towards stability and changes in breadwinning patterns among coupled households," Dependence Modeling, De Gruyter, vol. 7(1), pages 234-246, January.
  7. Francesco Bartolucci & Fulvia Pennoni, 2019. "Comment on: The class of CUB models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 437-439, September.
  8. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2016. "Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies," Journal of Educational and Behavioral Statistics, , vol. 41(2), pages 146-179, April.
  9. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
  10. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 484-486, September.
  11. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2011. "Assessment of School Performance Through a Multilevel Latent Markov Rasch Model," Journal of Educational and Behavioral Statistics, , vol. 36(4), pages 491-522, August.
  12. Francesco Bartolucci & Fulvia Pennoni, 2007. "A Class of Latent Markov Models for Capture–Recapture Data Allowing for Time, Heterogeneity, and Behavior Effects," Biometrics, The International Biometric Society, vol. 63(2), pages 568-578, June.
  13. Donald A Brand & Michaela Saisana & Lisa A Rynn & Fulvia Pennoni & Albert B Lowenfels, 2007. "Comparative Analysis of Alcohol Control Policies in 30 Countries," PLOS Medicine, Public Library of Science, vol. 4(4), pages 1-8, April.
  14. Francesco Bartolucci & Fulvia Pennoni & Brian Francis, 2007. "A latent Markov model for detecting patterns of criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 115-132, January.
  15. Francesco Bartolucci & Fulvia Pennoni, 2007. "On the approximation of the quadratic exponential distribution in a latent variable context," Biometrika, Biometrika Trust, vol. 94(3), pages 745-754.

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. Pennoni, Fulvia & Bartolucci, Francesco & Forte, Gianfranco & Ametrano, Ferdinando, 2020. "Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model," MPRA Paper 106150, University Library of Munich, Germany.

    Cited by:

    1. Pennoni, Fulvia & Bartolucci, Francesco & Forte, Gianfranco & Ametrano, Ferdinando, 2020. "Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model," MPRA Paper 106150, University Library of Munich, Germany.
    2. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).
    3. Žikica Lukić & Bojana Milošević, 2024. "A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(5), pages 797-820, October.

  2. Nakai, Miki & Pennoni, Fulvia, 2018. "A latent class analysis towards stability and changes in breadwinning patterns among coupled households," MPRA Paper 89950, University Library of Munich, Germany.

    Cited by:

    1. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.

  3. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.

    Cited by:

    1. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    2. Giovanni Piumatti, 2020. "Longitudinal Trends in Self-Rated Health During Times of Economic Uncertainty in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(2), pages 599-633, April.
    3. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    4. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.

  4. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.

    Cited by:

    1. Pantelis Samartsidis & Shaun R. Seaman & Silvia Montagna & André Charlett & Matthew Hickman & Daniela De Angelis, 2020. "A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1437-1459, October.
    2. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    3. F. J. Clouth & S. Pauws & F. Mols & J. K. Vermunt, 2022. "A new three-step method for using inverse propensity weighting with latent class analysis," 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(2), pages 351-371, June.
    4. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    5. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    6. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2023. "A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 387-419, August.

  5. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.

    Cited by:

    1. Gordon Anderson, Alessio Farcomeni, Maria Grazia Pittau and Roberto Zelli, 2019. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development Since 1990," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(1), pages 1-22, March.
    2. Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.
    3. Esther Acquah & Lorenzo Carbonari & Alessio Farcomeni & Giovanni Trovato, 2021. "Institutions and Economic Development: New Measurements and Evidence," CEIS Research Paper 521, Tor Vergata University, CEIS, revised 03 Nov 2021.
    4. Lorenzo Carbonari & Alessio Farcomeni & Cosimo Petracchi & Giovanni Trovato, 2024. "Macroprudential Policies and Credit Volatility," Working Paper series 24-16, Rimini Centre for Economic Analysis.
    5. Antonio Punzo & Salvatore Ingrassia & Antonello Maruotti, 2021. "Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions," Statistical Papers, Springer, vol. 62(3), pages 1519-1555, June.
    6. 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.
    7. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.
    8. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    9. Luca Brusa & Francesco Bartolucci & Fulvia Pennoni, 2023. "Tempered expectation-maximization algorithm for the estimation of discrete latent variable models," Computational Statistics, Springer, vol. 38(3), pages 1391-1424, September.
    10. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    11. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    12. Alessio Farcomeni, 2015. "Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1127-1135, December.
    13. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    14. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.
    15. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
    16. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    17. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    18. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    19. Gordon Anderson & Alessio Farcomeni & Grazia Pittau & Roberto Zelli, 2017. "Rectangular latent Markov models for time-specific clustering," Working Papers tecipa-589, University of Toronto, Department of Economics.
    20. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    21. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    22. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," 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(2), pages 273-300, June.
    23. Ulf Böckenholt & Blakeley McShane, 2014. "Comments on: Latent Markov models: a review of the general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 469-472, September.
    24. Ingmar Visser & Maarten Speekenbrink, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 478-483, September.
    25. Prateek Bansal & Daniel Hörcher & Daniel J. Graham, 2022. "A dynamic choice model to estimate the user cost of crowding with large‐scale transit data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 615-639, April.
    26. Lekkas, Peter & Paquet, Catherine & Howard, Natasha J. & Daniel, Mark, 2017. "Illuminating the lifecourse of place in the longitudinal study of neighbourhoods and health," Social Science & Medicine, Elsevier, vol. 177(C), pages 239-247.
    27. Hans Jørn Juhl & Morten H. J. Fenger & John Thøgersen, 2017. "Will the Consistent Organic Food Consumer Step Forward? An Empirical Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(3), pages 519-535.
    28. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2023. "A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 387-419, August.

  6. Pennoni, Fulvia & Tarantola, Stefano & Latvala, Ari, 2006. "The 2005 European e-Business Readiness Index," MPRA Paper 18048, University Library of Munich, Germany.

    Cited by:

    1. Reggi, Luigi & Arduini, Davide & Biagetti, Marco & Zanfei, Antonello, 2014. "How advanced are Italian regions in terms of public e-services? The construction of a composite indicator to analyze patterns of innovation diffusion in the public sector," Telecommunications Policy, Elsevier, vol. 38(5), pages 514-529.
    2. Salvatore Greco & Alessio Ishizaka & Menelaos Tasiou & Gianpiero Torrisi, 2019. "On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 61-94, January.

Articles

  1. Fulvia Pennoni & Francesco Bartolucci & Gianfranco Forte & Ferdinando Ametrano, 2022. "Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(1), February.
    See citations under working paper version above.
  2. Francesca Bassi & Fulvia Pennoni & Luca Rossetto, 2020. "The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 542-567, October.

    Cited by:

    1. Desjana Grymshi & Eva Crespo‐Cebada & Ahmed Elghannam & Francisco J. Mesías & Carlos Díaz‐Caro, 2022. "Understanding consumer attitudes towards ecolabeled food products: A latent class analysis regarding their purchasing motivations," Agribusiness, John Wiley & Sons, Ltd., vol. 38(1), pages 93-107, January.

  3. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.

    Cited by:

    1. Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.

  4. Pennoni Fulvia & Nakai Miki, 2019. "A latent class analysis towards stability and changes in breadwinning patterns among coupled households," Dependence Modeling, De Gruyter, vol. 7(1), pages 234-246, January.
    See citations under working paper version above.
  5. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2016. "Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies," Journal of Educational and Behavioral Statistics, , vol. 41(2), pages 146-179, April.
    See citations under working paper version above.
  6. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
    See citations under working paper version above.
  7. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 484-486, September.

    Cited by:

    1. Gordon Anderson, Alessio Farcomeni, Maria Grazia Pittau and Roberto Zelli, 2019. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development Since 1990," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(1), pages 1-22, March.
    2. Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.
    3. Esther Acquah & Lorenzo Carbonari & Alessio Farcomeni & Giovanni Trovato, 2021. "Institutions and Economic Development: New Measurements and Evidence," CEIS Research Paper 521, Tor Vergata University, CEIS, revised 03 Nov 2021.
    4. Lorenzo Carbonari & Alessio Farcomeni & Cosimo Petracchi & Giovanni Trovato, 2024. "Macroprudential Policies and Credit Volatility," Working Paper series 24-16, Rimini Centre for Economic Analysis.
    5. Antonio Punzo & Salvatore Ingrassia & Antonello Maruotti, 2021. "Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions," Statistical Papers, Springer, vol. 62(3), pages 1519-1555, June.
    6. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    7. Luca Brusa & Francesco Bartolucci & Fulvia Pennoni, 2023. "Tempered expectation-maximization algorithm for the estimation of discrete latent variable models," Computational Statistics, Springer, vol. 38(3), pages 1391-1424, September.
    8. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    9. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    10. Alessio Farcomeni, 2015. "Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1127-1135, December.
    11. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    12. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.
    13. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
    14. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    15. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    16. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    17. Gordon Anderson & Alessio Farcomeni & Grazia Pittau & Roberto Zelli, 2017. "Rectangular latent Markov models for time-specific clustering," Working Papers tecipa-589, University of Toronto, Department of Economics.
    18. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    19. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    20. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," 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(2), pages 273-300, June.
    21. Ulf Böckenholt & Blakeley McShane, 2014. "Comments on: Latent Markov models: a review of the general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 469-472, September.
    22. Ingmar Visser & Maarten Speekenbrink, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 478-483, September.
    23. Prateek Bansal & Daniel Hörcher & Daniel J. Graham, 2022. "A dynamic choice model to estimate the user cost of crowding with large‐scale transit data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 615-639, April.
    24. Lekkas, Peter & Paquet, Catherine & Howard, Natasha J. & Daniel, Mark, 2017. "Illuminating the lifecourse of place in the longitudinal study of neighbourhoods and health," Social Science & Medicine, Elsevier, vol. 177(C), pages 239-247.
    25. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2023. "A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 387-419, August.

  8. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2011. "Assessment of School Performance Through a Multilevel Latent Markov Rasch Model," Journal of Educational and Behavioral Statistics, , vol. 36(4), pages 491-522, August.

    Cited by:

    1. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    2. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.
    3. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    4. Bartolucci, Francesco & Lupparelli, Monia, 2012. "Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data," MPRA Paper 40588, University Library of Munich, Germany.
    5. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    6. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 484-486, September.
    7. Giorgio E. Montanari & Marco Doretti, 2019. "Ranking Nursing Homes’ Performances Through a Latent Markov Model with Fixed and Random Effects," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 307-326, November.
    8. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
    9. Francesca Bassi & Bruno Scarpa, 2015. "Latent class modeling of markers of day-specific fertility," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 263-276, August.
    10. Giorgio Eduardo Montanari & Marco Doretti & Maria Francesca Marino, 2022. "Model-based two-way clustering of second-level units in ordinal multilevel latent Markov 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. 16(2), pages 457-485, June.
    11. Sun-Joo Cho & Amanda P. Goodwin, 2017. "Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 846-870, September.
    12. Montanari, Giorgio E. & Doretti, Marco & Bartolucci, Francesco, 2017. "A multilevel latent Markov model for the evaluation of nursing homes' performance," MPRA Paper 80691, University Library of Munich, Germany.
    13. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    14. Yang Liu & Xiaojing Wang, 2020. "Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 274-296, June.
    15. Ruijin Lu & Tonja R. Nansel & Zhen Chen, 2023. "A Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 288-308, April.
    16. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    17. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2023. "A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 387-419, August.

  9. Francesco Bartolucci & Fulvia Pennoni, 2007. "A Class of Latent Markov Models for Capture–Recapture Data Allowing for Time, Heterogeneity, and Behavior Effects," Biometrics, The International Biometric Society, vol. 63(2), pages 568-578, June.

    Cited by:

    1. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    2. Antonio Acconcia & Maria Carannante & Michelangelo Misuraca & Germana Scepi, 2020. "Measuring Vulnerability to Poverty with Latent Transition Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(1), pages 1-31, August.
    3. Danilo Fegatelli & Luca Tardella, 2013. "Improved inference on capture recapture models with behavioural effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 45-66, March.
    4. Francesco Bartolucci & Monia Lupparelli, 2008. "Focused Information Criterion for Capture–Recapture Models for Closed Populations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 629-649, December.
    5. Danilo Alunni Fegatelli & Luca Tardella, 2016. "Flexible behavioral capture–recapture modeling," Biometrics, The International Biometric Society, vol. 72(1), pages 125-135, March.

  10. Donald A Brand & Michaela Saisana & Lisa A Rynn & Fulvia Pennoni & Albert B Lowenfels, 2007. "Comparative Analysis of Alcohol Control Policies in 30 Countries," PLOS Medicine, Public Library of Science, vol. 4(4), pages 1-8, April.

    Cited by:

    1. Seo, Seonwha & Chun, Sungsoo & Newell, Maxine & Yun, Mieun, 2015. "Korean public opinion on alcohol control policy: A cross-sectional International Alcohol Control study," Health Policy, Elsevier, vol. 119(1), pages 33-43.
    2. Saisana, Michaela & d'Hombres, Béatrice & Saltelli, Andrea, 2011. "Rickety numbers: Volatility of university rankings and policy implications," Research Policy, Elsevier, vol. 40(1), pages 165-177, February.
    3. Eric L. Sevigny & Michaela Saisana, 2016. "Measuring Interstate Variations in the Consequences of Illegal Drugs: A Composite Indicator Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(2), pages 501-529, September.
    4. Lorena Charrier & Michela Bersia & Alessio Vieno & Rosanna Irene Comoretto & Mindaugas Štelemėkas & Paola Nardone & Tibor Baška & Paola Dalmasso & Paola Berchialla, 2022. "Forecasting Frequent Alcohol Use among Adolescents in HBSC Countries: A Bayesian Framework for Making Predictions," IJERPH, MDPI, vol. 19(5), pages 1-14, February.
    5. Issaka Dialga, 2017. "A Sustainability Index of Mining Countries," Working Papers halshs-01500643, HAL.
    6. Byomkesh Talukder & Keith W. Hipel & Gary W. vanLoon, 2017. "Developing Composite Indicators for Agricultural Sustainability Assessment: Effect of Normalization and Aggregation Techniques," Resources, MDPI, vol. 6(4), pages 1-27, November.
    7. Kabir Dasgupta & Christopher Erwin & Alexander Plum, 2020. "The Devil is in the Details: Identifying the Unbiased Link between Access to Alcohol and Criminal Behavior," Working Papers 2020-12, Auckland University of Technology, Department of Economics.
    8. Gianni Onesti, 2020. "The Administrative Capacity of the Italian Regions from a Comparative Perspective," Rivista economica del Mezzogiorno, Società editrice il Mulino, issue 3, pages 513-538.
    9. Hayat, Azmat & Mohd Shafiai, Dr Muhammad Hakimi & Latif Samian, Abdul, 2018. "Alcoholic Drinks and Public Interest in case of Noe-liberalism and Islam," MPRA Paper 85492, University Library of Munich, Germany.
    10. Kozyk Vasyl & Vorobets Stepan & Musiiovska Oksana, 2019. "Research of the place of Ukraine in implementation of the goals of the sustainable development model," Technology audit and production reserves, 3(47) 2019, Socionet;Technology audit and production reserves, vol. 3(4(47)), pages 19-27.
    11. Hayat, Azmat & Mohd Shafiai, Dr Muhammad Hakimi & Haron, Mohamad Sabri & Shah, Sy. Munawar, 2018. "Alcoholic Drinks and Public Interest in case of Noe-liberalism and Islam," MPRA Paper 86893, University Library of Munich, Germany.
    12. Davison, Colleen M. & Ford, Catherine S. & Peters, Paul A. & Hawe, Penelope, 2011. "Community-driven alcohol policy in Canada's northern territories 1970-2008," Health Policy, Elsevier, vol. 102(1), pages 34-40, September.
    13. Nadia Minian & Anna Ivanova & Sabrina Voci & Scott Veldhuizen & Laurie Zawertailo & Dolly Baliunas & Aliya Noormohamed & Norman Giesbrecht & Peter Selby, 2020. "Computerized Clinical Decision Support System for Prompting Brief Alcohol Interventions with Treatment Seeking Smokers: A Sex-Based Secondary Analysis of a Cluster Randomized Trial," IJERPH, MDPI, vol. 17(3), pages 1-14, February.
    14. Umar, Maida & Asghar, Zahid, 2018. "SDG Index for Pakistan at Provincial Level," MPRA Paper 83997, University Library of Munich, Germany.
    15. Gisela Di Meglio & Metka Stare & Andrés Maroto & Luis Rubalcaba, 2015. "Public Services Performance: An Extended Framework and Empirical Assessment across the Enlarged EU," Environment and Planning C, , vol. 33(2), pages 321-341, April.

  11. Francesco Bartolucci & Fulvia Pennoni & Brian Francis, 2007. "A latent Markov model for detecting patterns of criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 115-132, January.

    Cited by:

    1. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    2. Eugene C.X. Ikejemba & Peter C. Schuur, 2018. "Analyzing the Impact of Theft and Vandalism in Relation to the Sustainability of Renewable Energy Development Projects in Sub-Saharan Africa," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    3. Brian Francis & Jiayi Liu, 2015. "Modelling escalation in crime seriousness: a latent variable approach," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 277-297, August.
    4. Paas, L.J. & Vermunt, J.K. & Bijmolt, T.H.A., 2007. "Discrete-time discrete-state latent Markov modelling for assessing and predicting household acquisitions of financial products," Other publications TiSEM 5781ab33-6687-4ad5-b57a-3, Tilburg University, School of Economics and Management.
    5. Catania, Leopoldo & Di Mari, Roberto, 2021. "Hierarchical Markov-switching models for multivariate integer-valued time-series," Journal of Econometrics, Elsevier, vol. 221(1), pages 118-137.
    6. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    7. Luca Brusa & Francesco Bartolucci & Fulvia Pennoni, 2023. "Tempered expectation-maximization algorithm for the estimation of discrete latent variable models," Computational Statistics, Springer, vol. 38(3), pages 1391-1424, September.
    8. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
    9. De Angelis, L & Paas, L.J., 2009. "The dynamic analysis and prediction of stock markets through the latent Markov model," Serie Research Memoranda 0053, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    10. Leonard J. Paas & Jeroen K. Vermunt & Tammo H. A. Bijmolt, 2007. "Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 955-974, October.
    11. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    12. S. Bacci & S. Pandolfi & F. Pennoni, 2014. "A comparison of some criteria for states selection in the latent Markov model for longitudinal 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. 8(2), pages 125-145, June.
    13. Yu Luo & David A. Stephens & Aman Verma & David L. Buckeridge, 2021. "Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records," Biometrics, The International Biometric Society, vol. 77(1), pages 78-90, March.
    14. 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.
    15. Siem Jan Koopman & Marius Ooms & André Lucas & Kees van Montfort & Victor Van Der Geest, 2008. "Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 104-130, February.

  12. Francesco Bartolucci & Fulvia Pennoni, 2007. "On the approximation of the quadratic exponential distribution in a latent variable context," Biometrika, Biometrika Trust, vol. 94(3), pages 745-754.

    Cited by:

    1. Bartolucci, Francesco & Nigro, Valentina, 2012. "Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data," Journal of Econometrics, Elsevier, vol. 170(1), pages 102-116.
    2. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

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 5 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 (5) 2012-06-05 2015-09-18 2015-09-26 2016-08-21 2021-02-22. Author is listed
  2. NEP-ORE: Operations Research (5) 2012-06-05 2015-09-18 2015-09-26 2016-08-21 2021-02-22. Author is listed
  3. NEP-DEM: Demographic Economics (1) 2018-02-05
  4. NEP-EUR: Microeconomic European Issues (1) 2018-02-05
  5. NEP-PAY: Payment Systems and Financial Technology (1) 2021-02-22
  6. NEP-RMG: Risk Management (1) 2021-02-22

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Fulvia Pennoni should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.