A0718
Title: Transformed function on scalar regression for random distribution
Authors: Hojin Yang - Pusan National University (Korea, South) [presenting]
Abstract: The aim is to develop a transformed function on the scalar regression model, using the functional principal components to account for random distribution. This framework allows us to model functions transformed from random distributions using the functional principal components approach in a transformed functional space and then regress functional principal component scores on multiple sets of predictors in their projected space. Thereby, the underlying model parameters, as well as the effect of the covariates in the projected space, can be estimated. Then, these parameters are transformed back to the original distributional space to understand the subject-specific random distributions. Hypothesis testing is also conducted, and predict random distributions for any given predictors are predicted.
