Modeling mixtures of distributions has rested on Gaussian distributions and/or a conditional independence hypothesis for a long time. Only recently have researchers begun to construct and study broader generic models without appealing to such hypotheses. Some of these extensions use copulas as a tool to build flexible models, as they permit modeling the dependence and the marginal distributions separately. But this approach also has drawbacks. First, the practitioner has to make more arbitrary choices, and second, marginal misspecification may loom on the horizon. This paper aims at overcoming these limitations by presenting a copulabased mixture model which is semiparametric. Thanks to a location-shift hypothesis, semiparametric estimation, also, is feasible, allowing for data adaptation without any modeling effort.
Journal of Classification – Springer Journals
Published: Oct 19, 2017
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