IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v23y2014i3p466-468.html
   My bibliography  Save this article

Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates

Author

Listed:
  • Silvia Bianconcini

Abstract

No abstract is available for this item.

Suggested Citation

  • Silvia Bianconcini, 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 466-468, September.
    • Handle: RePEc:spr:testjl:v:23:y:2014:i:3:p:466-468
    DOI: 10.1007/s11749-014-0390-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11749-014-0390-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11749-014-0390-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Beth A. Reboussin & Edward H. Ip & Mark Wolfson, 2008. "Locally dependent latent class models with covariates: an application to under‐age drinking in the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 877-897, October.
    2. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.
    3. 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.
    4. Dunson, David B., 2003. "Dynamic Latent Trait Models for Multidimensional Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 555-563, January.
    Full references (including those not matched with items on IDEAS)

    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.
    1. Xia, Ye-Mao & Tang, Nian-Sheng & Gou, Jian-Wei, 2016. "Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 259-275.
    2. Joan Gil & Paolo Li Donni & Eugenio Zucchelli, 2019. "Uncontrolled diabetes and health care utilisation: A bivariate latent Markov model approach," Health Economics, John Wiley & Sons, Ltd., vol. 28(11), pages 1262-1276, November.
    3. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    4. Brown, Sarah & Greene, William H. & Harris, Mark N. & Taylor, Karl, 2015. "An inverse hyperbolic sine heteroskedastic latent class panel tobit model: An application to modelling charitable donations," Economic Modelling, Elsevier, vol. 50(C), pages 228-236.
    5. William H. Greene & Mark N. Harris & Rachel J. Knott & Nigel Rice, 2021. "Specification and testing of hierarchical ordered response models with anchoring vignettes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 31-64, January.
    6. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    7. Franco Peracchi & Claudio Rossetti, 2022. "A nonlinear dynamic factor model of health and medical treatment," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1046-1066, June.
    8. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
    9. Michele Bavaro & Federico Tullio, 2023. "Intergenerational mobility measurement with latent transition matrices," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 25-45, March.
    10. Brown, Sarah & Greene, William H. & Harris, Mark N., 2014. "A New Formulation for Latent Class Models," IZA Discussion Papers 8283, Institute of Labor Economics (IZA).
    11. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.
    12. Liu, Hefei & Song, Xinyuan & Zhang, Baoxue, 2022. "Varying-coefficient hidden Markov models with zero-effect regions," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    13. Vana-Gür, Laura, 2024. "Multivariate ordinal regression for multiple repeated measurements," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).
    14. 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.
    15. H. Zhang & Q. Yu & C. Feng & D. Gunzler & P. Wu & X. M. Tu, 2012. "A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 2067-2079, June.
    16. Song, Xin-Yuan & Tang, Nian-Sheng & Chow, Sy-Miin, 2012. "A Bayesian approach for generalized random coefficient structural equation models for longitudinal data with adjacent time effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4190-4203.
    17. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    18. Mariana De Santish & María Inés Larai & Andrea Carrazana Riveraj & María Noelia Garberok & Carolina Judith Castroff, 2020. "Binge Drinking and Risk Preferences: an application to college students in Argentina," Asociación Argentina de Economía Política: Working Papers 4337, Asociación Argentina de Economía Política.
    19. repec:dau:papers:123456789/7331 is not listed on IDEAS
    20. 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.
    21. Stéphane Gregoir & Tristan‐Pierre Maury, 2013. "The Impact Of Social Housing On The Labour Market Status Of The Disabled," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1124-1138, September.

    More about this item

    Statistics

    Access and download statistics

    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:466-468. 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.

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