دورية أكاديمية
A simple and fast alternative to the EM algorithm for incomplete categorical data and latent class models
| العنوان: | A simple and fast alternative to the EM algorithm for incomplete categorical data and latent class models |
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| المؤلفون: | Galecki, Andrzej T., Ten Have, Thomas R., Molenberghs, Geert |
| بيانات النشر: | ELSEVIER SCIENCE BV |
| سنة النشر: | 2001 |
| المجموعة: | Document Server@UHasselt (Universiteit Hasselt) |
| مصطلحات موضوعية: | Missing data, Categorical data, Longitudinal data, multivariate marginal logistic models, latent class models, incomplete data, coarsening |
| الوصف: | Incomplete categorical data and latent class models play an important role in biostatistical and medical literature. The most common maximum likelihood procedure for accommodating these types of models is the EM algorithm. We present a faster alternative to these EM approaches that improves upon a recently introduced maximum likelihood-based alternative by Molenberghs and Goetghebeur (1997. J. Roy. Statist. Soc. Ser. B 59, 401–414) in two ways: by accommodating higher-dimensional problems via more time points in longitudinal problems and by employing a less tedious iteratively reweighted least-squares (IRLS) approach than the Newton–Raphson procedure used by MG. This IRLS approach also will facilitate the potential extension to models with random effects in the context of complete and incomplete categorical data and latent classes. We illustrate our method with a latent class application. As with the MG approach, we maximize the observed likelihood instead of the complete data likelihood under a multivariate generalized logistic model with composite link function. This results in a faster convergence rate than the EM algorithm, and allowing easily obtainable variance estimates. We illustrate the proposed estimation procedure using data from an HIV study involving four dichotomous tests measured on each individual, assuming a latent class disease variable with two levels. ; Authors are thankful to Drs. Stuart Baker and Mark Becker for their helpful comments and suggestions. Support for this research is in part by NIA grant No. P30 AG08808, P01 AG16699, and R29 CA531857, and by Belgian FWO-Vlaanderen Research Project “Sensitivity Analysis for Incomplete and Coarse Data”. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 0167-9473 |
| العلاقة: | Computational Statistics and Data Analysis, 35(3). p. 265-281; http://hdl.handle.net/1942/379Test; 281; 265; 35; 000166452000002 |
| DOI: | 10.1016/S0167-9473(00)00015-3 |
| الإتاحة: | https://doi.org/10.1016/S0167-9473Test(00)00015-3 http://hdl.handle.net/1942/379Test |
| حقوق: | (C) 2001 Elsevier Science B.V. All rights reserved. ; info:eu-repo/semantics/restrictedAccess |
| رقم الانضمام: | edsbas.858B0459 |
| قاعدة البيانات: | BASE |
| تدمد: | 01679473 |
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| DOI: | 10.1016/S0167-9473(00)00015-3 |