Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models

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In this paper, the component distributions in a multivariate mixture model are fitted using an exponential tilt model, in which 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 previous methods, which require the multivariate data to be discrete or require the data to be arbitrarily discretized, modeling can be performed based on the original data. We show that our approach produces estimates that are asymptotically normal and semiparametrically efficient. In addition, a model selection method is introduced for identifying the number of component distributions in the mixture model. The proposed methods are applied to a dataset from a reaction time task that is commonly used in developmental psychology research.



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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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