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Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models

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  • Keunbaik Lee
  • Sanggil Kang
  • Xuefeng Liu
  • Daekwan Seo
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    Abstract

    Likelihood-based marginalized models using random effects have become popular for analyzing longitudinal categorical data. These models permit direct interpretation of marginal mean parameters and characterize the serial dependence of longitudinal outcomes using random effects [12,22]. In this paper, we propose model that expands the use of previous models to accommodate longitudinal nominal data. Random effects using a new covariance matrix with a Kronecker product composition are used to explain serial and categorical dependence. The Quasi-Newton algorithm is developed for estimation. These proposed methods are illustrated with a real data set and compared with other standard methods.

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    File URL: http://hdl.handle.net/10.1080/02664763.2010.515675
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

    Volume (Year): 38 (2011)
    Issue (Month): 8 (July)
    Pages: 1577-1590

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    Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1577-1590

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    Cited by:
    1. Lee, Keunbaik & Lee, JungBok & Hagan, Joseph & Yoo, Jae Keun, 2012. "Modeling the random effects covariance matrix for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1545-1551.

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