A LATENT VARIABLE MIXTURE MODEL FOR COMPOSITION-ON-COMPOSITION REGRESSION WITH APPLICATION TO CHEMICAL RECYCLING.

ABSTRACT: It is quite common to encounter compositional data in a regression framework in data analysis. When both responses and predictors are compositional, most existing models rely on a family of log-ratio based transformations to move the analysis from the simplex to the reals. This often makes the interpretation of the model more complex. A transformation-free regression model was recently developed, but it only allows for a single compositional predictor. However, many datasets include multiple compositional predictors of interest. Motivated by an application to hydrothermal liquefaction (HTL) data, a novel extension of this transformation-free regression model is provided that allows for two (or more) compositional predictors to be used via a latent variable mixture. A modified expectation-m

SUBMITTER: Rios N 

PROVIDER: S-EPMC12448131 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature