Can I read Mixture Modeling on Related Samples by $\Psi$-stick Breaking and Kernel Perturbation on EtoBox?

Mixture Modeling on Related Samples by $\Psi$-stick Breaking and Kernel Perturbation by Soriano, Jacopo; Ma, Li is a scholarly article available to read on EtoBox.

What is Mixture Modeling on Related Samples by $\Psi$-stick Breaking and Kernel Perturbation about?

There has been great interest recently in applying nonparametric kernel mixtures in a hierarchical manner to model multiple related data samples jointly. In such settings several data features are commonly present: (i) the related samples often share some, if not all, of the mixture components but with differing weights, (ii) only some, not all, of the mixture components vary across the samples, and (iii) often the shared mixture components across samples are not aligned perfectly in terms of their location and spread, but rather display small misalignments either due to systematic cross-sample difference or more often due to uncontrolled, extraneous causes. Properly incorporating these features in mixture modeling will enhance the efficiency of inference, whereas ignoring them not only reduces efficiency but can jeopardize the validity of the inference due to issues such as confounding. We introduce two techniques for incorporating these features in modeling related data samples using kernel mixtures. The first technique, called $\psi$-stick breaking, is a joint generative process for the mixing weights through the breaking of both a stick shared by all the samples for the compone

Author

Soriano, Jacopo; Ma, Li

Published

2017

Language

EN