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Longitudinal nominal data analysis using marginalized models

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  • Lee, Keunbaik
  • Mercante, Donald

Abstract

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.

Suggested Citation

  • 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

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    1. 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.
    2. Diana Miglioretti & Patrick Heagerty, 2004. "Marginal Modeling of Multilevel Binary Data with Time-Varying Covariates," UW Biostatistics Working Paper Series 1050, Berkeley Electronic Press.
    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.
    4. Patrick J. Heagerty, 2002. "Marginalized Transition Models and Likelihood Inference for Longitudinal Categorical Data," Biometrics, The International Biometric Society, vol. 58(2), pages 342-351, June.
    5. Patrick J. Heagerty, 1999. "Marginally Specified Logistic-Normal Models for Longitudinal Binary Data," Biometrics, The International Biometric Society, vol. 55(3), pages 688-698, September.
    6. Keunbaik Lee & Michael J. Daniels, 2007. "A Class of Markov Models for Longitudinal Ordinal Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1060-1067, December.
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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 & Joo, Yongsung, 2019. "Marginalized models for longitudinal count data," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 47-58.
    3. Özgür Asar & Ozlem Ilk, 2016. "First-order marginalised transition random effects models with probit link function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 925-942, April.
    4. 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.

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