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Semiparametric mixture models and repeated measures: the multinomial cut point model

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  • I. R. Cruz‐Medina
  • T. P. Hettmansperger
  • H. Thomas

Abstract

Summary. Suppose that we have m repeated measures on each subject, and we model the observation vectors with a finite mixture model. We further assume that the repeated measures are conditionally independent. We present methods to estimate the shape of the component distributions along with various features of the component distributions such as the medians, means and variances. We make no distributional assumptions on the components; indeed, we allow different shapes for different components.

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  • I. R. Cruz‐Medina & T. P. Hettmansperger & H. Thomas, 2004. "Semiparametric mixture models and repeated measures: the multinomial cut point model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(3), pages 463-474, August.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:3:p:463-474
    DOI: 10.1111/j.1467-9876.2004.05203.x
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    Cited by:

    1. Hiroyuki Kasahara & Katsumi Shimotsu, 2014. "Non-parametric identification and estimation of the number of components in multivariate mixtures," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 97-111, January.
    2. Kasahara, Hiroyuki & 笠原, 博幸 & Shimotsu, Katsumi & 下津, 克己, 2010. "Nonparametric Identification of Multivariate Mixtures," Discussion Papers 2010-09, Graduate School of Economics, Hitotsubashi University.
    3. Jenifer Hamil-Luker & Angela O’rand, 2007. "Gender differences in the link between childhood socioeconomic conditions and heart attack risk in adulthood," Demography, Springer;Population Association of America (PAA), vol. 44(1), pages 137-158, February.

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