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


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


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.

Suggested Citation

  • Keunbaik Lee & Sanggil Kang & Xuefeng Liu & Daekwan Seo, 2011. "Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1577-1590, July.
  • Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1577-1590
    DOI: 10.1080/02664763.2010.515675

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    Cited by:

    1. Lee, Keunbaik & Sohn, Insuk & Kim, Donguk, 2016. "Analysis of long series of longitudinal ordinal data using marginalized models," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 363-371.
    2. 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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