Online Program Home
My Program

Abstract Details

Activity Number: 30
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #319080 View Presentation
Title: Functional Multiple Indicators, Multiple Causes Measurement Error Models
Author(s): Carmen Tekwe* and Roger Zoh and Raymond Carroll and Guoyao Wu and Fuller Bazer
Companies: Texas A&M University and Texas A&M University and Texas A&M University and Texas A&M University and Texas A&M University
Keywords: Basis functions ; Energy expenditure ; Functional principal components ; Latent variables ; Measurement error ; MIMIC models

In studying the pathway to obesity, a variation of the multiple indicators, multiple cause measurement error (MIMIC ME) models can be applied. In this paper, we define the functional MIMIC ME model by extending the linear MIMIC ME model to allow longitudinal responses that appear as curves over a given period of time. The mean curves are modeled using basis splines and functional principal components. We also propose a novel approach to identifying the classical measurement error associated with approximating true metabolic rate by heat production based on functional principal components. In addition to defining the FMIMIC ME model, we estimate the model parameters using the EM algorithm. We also provide a discussion of the model's identifiability. The model is applied to study the relationship between metabolic rate and the multiple indicators of energy expenditure among Zucker diabetic fatty rats. Results from a brief simulation study are also provided.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association