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Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models

In: Modern Nonparametric, Robust and Multivariate Methods

Author

Listed:
  • Tracey W. Hammel

    (Penn State University, Department of Statistics)

  • Thomas P. Hettmansperger

    (Penn State University, Department of Statistics)

  • Denis H. Y. Leung

    (Singapore Management University, School of Economics)

  • Jing Qin

    (National Institute of Allergy and Infectious Diseases, Biostatistics Research Branch)

Abstract

The conditional independence assumption is commonly used in multivariate mixture models in behavioral research. We propose an exponential tilt model to analyze data from a multivariate mixture distribution with conditionally independent components. In this model, the log ratio of the density functions of the components is modeled as a quadratic function in the observations. There are a number of advantages in this approach. First, except for the exponential tilt assumption, the marginal distributions of the observations can be completely arbitrary. Second, unlike some previous methods, which require the multivariate data to be discrete, modeling can be performed based on the original data.

Suggested Citation

  • Tracey W. Hammel & Thomas P. Hettmansperger & Denis H. Y. Leung & Jing Qin, 2015. "Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models," Springer Books, in: Klaus Nordhausen & Sara Taskinen (ed.), Modern Nonparametric, Robust and Multivariate Methods, edition 1, chapter 0, pages 371-392, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-22404-6_21
    DOI: 10.1007/978-3-319-22404-6_21
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