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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

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. Copyright 2004 Royal Statistical Society.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jorssc:v:53:y:2004:i:3:p:463-474
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    File URL: http://www.blackwell-synergy.com/doi/abs/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.

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