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Longitudinal nominal data analysis using marginalized models


  • Lee, Keunbaik
  • Mercante, Donald


Recently, marginalized transition models have become popular for the analysis of longitudinal data. Heagerty (2002) and Lee and Daniels (2007) proposed marginalized transition models for the analysis of longitudinal binary data and ordinal data, respectively. In this paper, we extend their work to accommodate longitudinal nominal data using a Markovian dependence structure. A Fisher-scoring algorithm is developed for estimation. Methods are illustrated with a real dataset and are compared with other standard methods.

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  • Lee, Keunbaik & Mercante, Donald, 2010. "Longitudinal nominal data analysis using marginalized models," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 208-218, January.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:208-218

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    References listed on IDEAS

    1. Diana Miglioretti & Patrick Heagerty, 2004. "Marginal Modeling of Multilevel Binary Data with Time-Varying Covariates," UW Biostatistics Working Paper Series 1050, Berkeley Electronic Press.
    2. Theil, Henri, 1969. "A Multinomial Extension of the Linear Logit Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(3), pages 251-259, October.
    3. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
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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.

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