A nested expectation–maximization algorithm for latent class models with covariates
Author
Abstract
Suggested Citation
DOI: 10.1016/j.spl.2018.10.015
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
- Hunter D.R. & Lange K., 2004. "A Tutorial on MM Algorithms," The American Statistician, American Statistical Association, vol. 58, pages 30-37, February.
- Dankmar Böhning & Bruce Lindsay, 1988. "Monotonicity of quadratic-approximation algorithms," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 40(4), pages 641-663, December.
- Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
- Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
- Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
- Nicholas G. Polson & James G. Scott & Jesse Windle, 2013. "Bayesian Inference for Logistic Models Using Pólya--Gamma Latent Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1339-1349, December.
- Peter G. M. van der Heijden & Jos Dessens & UIf Bockenholt, 1996. "Estimating the Concomitant-Variable Latent-Class Model With the EM Algorithm," Journal of Educational and Behavioral Statistics, , vol. 21(3), pages 215-229, September.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Počuča, Nikola & Jevtić, Petar & McNicholas, Paul D. & Miljkovic, Tatjana, 2020. "Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 79-93.
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.- Bertrand, Aurélie & Hafner, Christian M., 2011. "On heterogeneous latent class models with applications to the analysis of rating scores," SFB 649 Discussion Papers 2011-062, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
- 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.
- Aurélie Bertrand & Christian M. Hafner, 2011. "On heterogeneous latent class models with applications to the analysis of rating scores," SFB 649 Discussion Papers SFB649DP2011-062, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Bertrand, Aurelie & Hafner, Christian, 2014. "On heterogeneous latent class models with applications to the analysis of rating scores," LIDAM Reprints ISBA 2014027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Bertrand, Aurelie & Hafner, Christian, 2011. "On heterogeneous latent class models with applications to the analysis of rating scores," LIDAM Discussion Papers ISBA 2011028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Jennifer Oser & Marc Hooghe & Zsuzsa Bakk & Roberto Mari, 2023. "Changing citizenship norms among adolescents, 1999-2009-2016: A two-step latent class approach with measurement equivalence testing," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4915-4933, October.
- Aely Park & Youngmi Kim & Jennifer Murphy, 2023. "Adverse Childhood Experiences and Substance Use Among Korean College Students: Different by Gender?," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 16(4), pages 1811-1825, August.
- Bakk, Zsuzsa & Kuha, Jouni, 2020. "Relating latent class membership to external variables: an overview," LSE Research Online Documents on Economics 107564, London School of Economics and Political Science, LSE Library.
- Gugerty, Mary Kay & Mitchell, George E. & Santamarina, Francisco J., 2021. "Discourses of evaluation: Institutional logics and organizational practices among international development agencies," World Development, Elsevier, vol. 146(C).
- Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
- Sylvia Frühwirth-Schnatter & Gertraud Malsiner-Walli, 2019. "From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering," 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 33-64, March.
- Tian, Guo-Liang & Tang, Man-Lai & Liu, Chunling, 2012. "Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 255-265.
- Yajing Zhu & Fiona Steele & Irini Moustaki, 2020. "A multilevel structural equation model for the interrelationships between multiple latent dimensions of childhood socio‐economic circumstances, partnership transitions and mid‐life health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1029-1050, June.
- Jason V. Chen & Kurt H. Gee & Jed J. Neilson, 2021. "Disclosure Prominence and the Quality of Non‐GAAP Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 59(1), pages 163-213, March.
- Brewsaugh, Katrina & Masyn, Katherine E. & Salloum, Alison, 2018. "Child welfare workers' sexism and beliefs about father involvement," Children and Youth Services Review, Elsevier, vol. 89(C), pages 132-144.
- Kim, Hyun Ju & Chung, Jae Young, 2020. "Factors affecting youth citizenship in accordance with socioeconomic background," Children and Youth Services Review, Elsevier, vol. 111(C).
- Qianru Liang & Jimmy de la Torre & Nancy Law, 2023. "Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 690-718, December.
- Sunil Kumar & Apurba Vishal Dabgotra, 2021. "A latent class analysis on the usage of mobile phones among management students," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 89-114, March.
- Tobias A. Bauer & Alexandro Folster & Tina Braun & Timo von Oertzen, 2021. "A Group Comparison Test under Uncertain Group Membership," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 920-937, December.
- Bartolucci, Francesco & Montanari, Giorgio E. & Pandolfi, Silvia, 2015. "Three-step estimation of latent Markov models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 287-301.
- Ewa Genge, 2014. "A latent class analysis of the public attitude towards the euro adoption in Poland," 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(4), pages 427-442, December.
- Margot Bennink & Marcel A. Croon & Brigitte Kroon & Jeroen K. Vermunt, 2016. "Micro–macro multilevel latent class models with multiple discrete individual-level variables," 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. 10(2), pages 139-154, June.
More about this item
Keywords
em algorithm; Latent class model; Multivariate categorical data; Pólya-gamma;All these keywords.
Statistics
Access and download statisticsCorrections
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:146:y:2019:i:c:p:97-103. 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.