Estimation in the Mixture of Markov Chains Moving With Different Speeds
Author
Suggested Citation
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Surya, Budhi Arta, 2022. "Conditional multivariate distributions of phase-type for a finite mixture of Markov jump processes given observations of sample path," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
- Sylvia Frühwirth‐Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter‐Ebmer, 2012.
"Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1116-1137, November.
- Sylvia Frühwirth-Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter-Ebmer, 2010. "Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering," NRN working papers 2010-14, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
- Sylvia Frühwirth-Schnatter & Andrea Weber & Rudolf Winter-Ebmer, 2010. "Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering," Economics working papers 2010-11, Department of Economics, Johannes Kepler University Linz, Austria.
- Roy Costilla & Ivy Liu & Richard Arnold & Daniel Fernández, 2019. "Bayesian model-based clustering for longitudinal ordinal data," Computational Statistics, Springer, vol. 34(3), pages 1015-1038, September.
- Legrand D. F. Saint-Cyr & Laurent Piet, 2017.
"Movers and stayers in the farming sector: accounting for unobserved heterogeneity in structural change,"
Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 777-795, August.
- Saint-Cyr, Legrand D.F. & Piet, Laurent, 2015. "Movers and Stayers in the Farming Sector: Accounting for Unobserved Heterogeneity in Structural Change," 89th Annual Conference, April 13-15, 2015, Warwick University, Coventry, UK 204234, Agricultural Economics Society.
- Legrand D.F. Saint-Cyr & Laurent Piet, 2015. "Movers and stayers in the farming sector: accounting for unobserved heterogeneity in structural change," Working Papers SMART 15-06, INRAE UMR SMART.
- Saint-Cyr, Legrand D.F. & Piet, Laurent, 2015. "Movers and stayers in the farming sector: accounting for unobserved heterogeneity in structural change," Working Papers 208912, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
- Voß, Sebastian & Weißbach, Rafael, 2014. "A score-test on measurement errors in rating transition times," Journal of Econometrics, Elsevier, vol. 180(1), pages 16-29.
- Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
- Saint-Cyr, Legrand D. F. & Piet, Laurent, 2014. "Movers and Stayers in the Farming Sector: Another Look at Heterogeneity in Structural Change," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 183068, European Association of Agricultural Economists.
- Sylvia Frühwirth-Schnatter & Stefan Pittner & Andrea Weber & Rudolf Winter-Ebmer, 2016.
"Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering,"
Economics working papers
2016-10, Department of Economics, Johannes Kepler University Linz, Austria.
- Frühwirth-Schnatter, Sylvia & Pittner, Stefan & Weber, Andrea & Winter-Ebmer, Rudolf, 2016. "Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering," Economics Series 324, Institute for Advanced Studies.
- Sylvia Frühwirth-Schnatter & Stefan Pittner & Andrea Weber & Rudolf Winter-Ebmer, 2016. "Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering," CDL Aging, Health, Labor working papers 2016-06, The Christian Doppler (CD) Laboratory Aging, Health, and the Labor Market, Johannes Kepler University Linz, Austria.
- Johannes Hörner & Nicolas S Lambert, 2021.
"Motivational Ratings [Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions],"
The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(4), pages 1892-1935.
- Johannes Horner & Nicolas Lambert, 2016. "Motivational Ratings," Cowles Foundation Discussion Papers 2035, Cowles Foundation for Research in Economics, Yale University.
- Johannes Hörner & Nicolas Lambert, 2021. "Motivational Ratings," Post-Print hal-03759599, HAL.
- Hörner, Johannes & Lambert, Nicolas, 2020. "Motivational Ratings," TSE Working Papers 20-1134, Toulouse School of Economics (TSE).
- Johannes Hörner & Nicolas Lambert, 2021. "Motivational Ratings," Working Papers hal-03187510, HAL.
- Fitzpatrick, Matthew & Stewart, Michael, 2022. "Asymptotics for Markov chain mixture detection," Econometrics and Statistics, Elsevier, vol. 22(C), pages 56-66.
- Budhi Surya, 2021. "A new class of conditional Markov jump processes with regime switching and path dependence: properties and maximum likelihood estimation," Papers 2107.07026, arXiv.org.
- Frydman, Halina & Schuermann, Til, 2008. "Credit rating dynamics and Markov mixture models," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1062-1075, June.
- Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate 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. 13(1), pages 117-143, March.
- Sylvia Frühwirth-Schnatter, 2011. "Panel data analysis: a survey on model-based clustering of time series," 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 251-280, December.
- Sylvia Frühwirth-Schnatter & Christoph Pamminger, 2009. "Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models," NRN working papers 2009-07, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
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:bes:jnlasa:v:100:y:2005:p:1046-1053. 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.
We have no bibliographic references for this item. You can help adding them by using 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: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.