A0684
Title: Regression modeling of zero-inflated functional data
Authors: Anbin Rhee - Virginia Tech (United States)
Pang Du - Virginia Tech (United States) [presenting]
Abstract: Zero-inflated functional data appear when an excessive number of zeros are recorded for some functional variables due to the threshold of detection limits. To analyze this kind of data, a two-part mixed-effects functional regression model is proposed. The first part models the probability function of the functional response taking nonzero values via a mixed-effects functional logistic regression model. The second part models the log-transformed true response function by a mixed-effects functional linear model. Smoothing splines are used to estimate both the fixed and random effect functions. The estimation procedures for the two parts are respectively penalized quasi-likelihood and a REML-based EM algorithm. Extensive simulations are presented to evaluate the numerical performance of the method. The method is also applied to a Northwestern ICU study to investigate the relationship between total calcium and albumin measurements in repeated blood tests during each of the multiple ICU visits of a patient. Results show that the proposed approach effectively handles zero inflation while recovering the functional relationship between the variables of interest.
