Likelihood-Based Approach for Analysis of Longitudinal Nominal Data Using Marginalized Random Effects Models
Document Type
Article
Publication Date
8-1-2011
Description
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.
Citation Information
Lee, Keunbaik; Kang, Sanggil; Liu, Xuefeng; and Seo, Daekwan. 2011. Likelihood-Based Approach for Analysis of Longitudinal Nominal Data Using Marginalized Random Effects Models. Journal of Applied Statistics. Vol.38(8). 1577-1590. https://doi.org/10.1080/02664763.2010.515675 ISSN: 0266-4763