IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v75y2005i3p211-218.html
   My bibliography  Save this article

On identifiability of certain latent class models

Author

Listed:
  • van Wieringen, Wessel N.

Abstract

Blischke [1962. Moment estimators for the parameters of a mixture of two binomial distributions. Ann. Math. Statist. 33, 444-454] studies a mixture of two binomials, a latent class model. In this article we generalize this model to a mixture of two products of binomials. We show when this generalized model is identifiable.

Suggested Citation

  • van Wieringen, Wessel N., 2005. "On identifiability of certain latent class models," Statistics & Probability Letters, Elsevier, vol. 75(3), pages 211-218, December.
  • Handle: RePEc:eee:stapro:v:75:y:2005:i:3:p:211-218
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(05)00228-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Anton K. Formann, 2003. "Latent Class Model Diagnosis from a Frequentist Point of View," Biometrics, The International Biometric Society, vol. 59(1), pages 189-196, March.
    2. Richard McHugh, 1956. "Efficient estimation and local identification in latent class analysis," Psychometrika, Springer;The Psychometric Society, vol. 21(4), pages 331-347, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Roberto Quinino & Linda Ho & Emílio Suyama, 2013. "Alternative estimator for the parameters of a mixture of two binomial distributions," Statistical Papers, Springer, vol. 54(1), pages 47-69, February.
    2. Beavers, Daniel P. & Stamey, James D., 2012. "Bayesian sample size determination for binary regression with a misclassified covariate and no gold standard," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2574-2582.

    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. Jesus Perez-Mayo, 2005. "Identifying deprivation profiles in Spain: a new approach," Applied Economics, Taylor & Francis Journals, vol. 37(8), pages 943-955.
    2. Fabrizia Mealli & Barbara Pacini & Elena Stanghellini, 2016. "Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs," Journal of Educational and Behavioral Statistics, , vol. 41(5), pages 463-480, October.
    3. 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.
    4. Anton Formann & Ivo Ponocny, 2002. "Latent change classes in dichotomous data," Psychometrika, Springer;The Psychometric Society, vol. 67(3), pages 437-457, September.
    5. 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.
    6. Patrício Soares Costa & Nadine Correia Santos & Pedro Cunha & Joana Almeida Palha & Nuno Sousa, 2013. "The Use of Bayesian Latent Class Cluster Models to Classify Patterns of Cognitive Performance in Healthy Ageing," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    7. Formann, Anton K., 2007. "Mixture analysis of multivariate categorical data with covariates and missing entries," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5236-5246, July.
    8. Evan Munro & Serena Ng, 2022. "Latent Dirichlet Analysis of Categorical Survey Responses," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 256-271, January.
    9. Qian-Li Xue & Karen Bandeen-Roche, 2002. "Combining Complete Multivariate Outcomes with Incomplete Covariate Information: A Latent Class Approach," Biometrics, The International Biometric Society, vol. 58(1), pages 110-120, March.
    10. Frank Rijmen & Paul Boeck & Han Maas, 2005. "An IRT Model with a Parameter-Driven Process for Change," Psychometrika, Springer;The Psychometric Society, vol. 70(4), pages 651-669, December.
    11. PEREZ MAYO Jésus, 2003. "Measuring deprivation in Spain," IRISS Working Paper Series 2003-09, IRISS at CEPS/INSTEAD.
    12. Guan-Hua Huang & Karen Bandeen-Roche, 2004. "Building an identifiable latent class model with covariate effects on underlying and measured variables," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 5-32, March.
    13. Paul Westers & Henk Kelderman, 1992. "Examining differential item functioning due to item difficulty and alternative attractiveness," Psychometrika, Springer;The Psychometric Society, vol. 57(1), pages 107-118, March.
    14. Aurélie Bertrand & Christian Hafner, 2014. "On heterogeneous latent class models with applications to the analysis of rating scores," Computational Statistics, Springer, vol. 29(1), pages 307-330, February.
    15. A. Felipe & P. Miranda & L. Pardo, 2015. "Minimum $$\phi $$ ϕ -Divergence Estimation in Constrained Latent Class Models for Binary Data," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 1020-1042, December.
    16. Beth A. Reboussin & Nicholas S. Ialongo, 2010. "Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 145-164, January.
    17. Evan M. Munro & Serena Ng, 2020. "Latent Dirichlet Analysis of Categorical Survey Expectations," NBER Working Papers 27182, National Bureau of Economic Research, Inc.
    18. Baffour Bernard & Brown James J. & Smith Peter W.F., 2021. "Latent Class Analysis for Estimating an Unknown Population Size – with Application to Censuses," Journal of Official Statistics, Sciendo, vol. 37(3), pages 673-697, September.
    19. Jing Ouyang & Gongjun Xu, 2022. "Identifiability of Latent Class Models with Covariates," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1343-1360, December.
    20. Yuqi Gu & Gongjun Xu, 2019. "The Sufficient and Necessary Condition for the Identifiability and Estimability of the DINA Model," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 468-483, June.

    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:eee:stapro:v:75:y:2005:i:3:p:211-218. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.