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.
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Acknowledgements
Tracey Wrobel Hammel and Thomas Hettmansperger were partially supported by NSF Grant SES-0518772. Denis Leung was supported by SMU Research Center.
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Hammel, T.W., Hettmansperger, T.P., Leung, D.H.Y., Qin, J. (2015). Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models. In: Nordhausen, K., Taskinen, S. (eds) Modern Nonparametric, Robust and Multivariate Methods. Springer, Cham. https://doi.org/10.1007/978-3-319-22404-6_21
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DOI: https://doi.org/10.1007/978-3-319-22404-6_21
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