Variable Selection for Skewed Model-Based Clustering: Application to the Identification of Novel Sleep Phenotypes.

ABSTRACT: In sleep research, applying finite mixture models to sleep characteristics captured 8 through multiple data types, including self-reported sleep diary, a wrist monitor capturing movement (actigraphy), and brain waves (polysomnography), may suggest new phenotypes that reflect underlying disease mechanisms. However, a direct mixture model application is challenging because there are many sleep variables from which to choose, and sleep variables are often highly skewed even in homogenous samples. Moreover, previous sleep research findings indicate that some of the most clinically interesting solutions will be those that incorporate all three data types. Thus, we present two novel skewed variable selection algorithms based on the multivariate skew normal (MSN) distribution: one that selects th

SUBMITTER: Wallace ML 

PROVIDER: S-EPMC6510512 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature