Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models.

ABSTRACT: Piecewise growth mixture models are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional form; examples include two linear phases of growth, or a quadratic phase followed by a linear phase. The changepoint (knot) is the time of transition from one developmental phase (segment) to another. Inferring the location of the changepoint(s) is often of practical interest, along with inference for other model parameters. A random changepoint allows for individual differences in the transition time within each class. The primary objectives of our study

SUBMITTER: Lock EF 

PROVIDER: S-EPMC6019237 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature