Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates
Author
Abstract
Suggested Citation
DOI: 10.1007/s11749-014-0387-1
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
- 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.
- 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.
- Tsukasa Hokimoto & Kunio Shimizu, 2014. "A non-homogeneous hidden Markov model for predicting the distribution of sea surface elevation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 294-319, February.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Thøgersen, John, 2017. "Housing-related lifestyle and energy saving: A multi-level approach," Energy Policy, Elsevier, vol. 102(C), pages 73-87.
- Thøgersen, John, 2018. "Transport-related lifestyle and environmentally-friendly travel mode choices: A multi-level approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 166-186.
- Klaus G. Grunert & Yanfeng Zhou & Marija Banovic & Natascha Loebnitz, 2021. "Supermarket competence in emergent markets: Conceptualization, measurement, effects, and policy implications," Journal of Consumer Affairs, Wiley Blackwell, vol. 55(4), pages 1633-1659, December.
- Leonard Paas & Tammo Bijmolt & Jeroen Vermunt, 2015. "Long-term developments of respondent financial product portfolios in the EU: a multilevel latent class analysis," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 249-262, August.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- 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.
- Simon DeDeo, 2016. "Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions," Future Internet, MDPI, vol. 8(3), pages 1-23, July.
- Marc A. Scott & Kaushik Mohan & Jacques‐Antoine Gauthier, 2020. "Model‐based clustering and analysis of life history data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1231-1251, June.
- Beatrice Foroni & Luca Merlo & Lea Petrella, 2023. "Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market," Papers 2307.06400, arXiv.org.
- Giorgio E. Montanari & Silvia Pandolfi, 2018. "Evaluation of long-term health care services through a latent Markov model with covariates," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 151-173, March.
- 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.
- 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.
- Danisman, Ozgur & Uzunoglu Kocer, Umay, 2021. "Hidden Markov models with binary dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
- Renske E. Kuijpers & Ingmar Visser & Dylan Molenaar, 2021. "Testing the Within-State Distribution in Mixture Models for Responses and Response Times," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 348-373, June.
- 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.
- 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.
- 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.
- Beatrice Foroni & Luca Merlo & Lea Petrella, 2023. "Expectile hidden Markov regression models for analyzing cryptocurrency returns," Papers 2301.09722, arXiv.org, revised Jan 2024.
- Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
- Valeriy Zakamulin, 2023. "Not all bull and bear markets are alike: insights from a five-state hidden semi-Markov model," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-25, March.
- Giner, Javier & Zakamulin, Valeriy, 2023. "A regime-switching model of stock returns with momentum and mean reversion," Economic Modelling, Elsevier, vol. 122(C).
- Geir D. Berentsen & Jan Bulla & Antonello Maruotti & Bård Støve, 2022. "Modelling clusters of corporate defaults: Regime‐switching models significantly reduce the contagion source," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 698-722, June.
- De Angelis Luca & Viroli Cinzia, 2017. "A Markov-switching regression model with non-Gaussian innovations: estimation and testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-22, April.
- 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.
- 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.
- Ichkitidze, Yuri, 2018. "Temporary price trends in the stock market with rational agents," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 103-117.
- 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.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:testjl:v:23:y:2014:i:3:p:473-477. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.