A0883
Title: Semiparametric bivariate hierarchical state space model with application to hormone circadian relationship
Authors: Mengying You - Shanghai University of International Business and Economics (China) [presenting]
Wensheng Guo - University of Pennsylvania (United States)
Abstract: The adrenocorticotropic hormone and cortisol are critical in stress regulation and the sleep-wake cycle. Most research has focused on how the two hormones regulate each other regarding short-term pulses. Few studies have been conducted on the circadian relationship between the two hormones and how it differs between normal and abnormal groups. The circadian patterns are difficult to model as parametric functions. Directly extending univariate functional mixed effects models would result in a large dimensional problem and a challenging nonparametric inference. A semi-parametric bivariate hierarchical state space model is proposed, in which a hierarchical state space model with a nonparametric population average and subject-specific components models each hormone profile. The bivariate relationship is constructed by concatenating two latent independent subject-specific random functions specified by a design matrix, leading to a parametric inference on the correlation. A computationally efficient state-space EM algorithm is proposed for estimation and inference. The proposed method is applied to a study of chronic fatigue syndrome and fibromyalgia. An erratic regulation pattern is discovered in the patient group in contrast to a circadian regulation pattern conforming to the day-night cycle in the control group.
