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Activity Number: 30
Type: Contributed
Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #308247
Title: Residuals in the Growth Curve Model and Their Application in the Analysis of Longitudinal Data
Author(s): Jemila Hamid*+ and WeiLiang Huang
Companies: McMaster University and McMaster University
Keywords: Residuals ; Growth Curve Model ; Longtudinal Data ; Decomposition ; Model Diagnostics
Abstract:

Statistical models often rely on several assumptions - distribution assumption on outcome variables as well as relationship assumptions that link the outcome and independent variables. Modeling is not complete without performing model diagnostics. Residuals play important roles in assessing model fit, validating model assumptions as well as identifying outliers and/or influential observations. In univariate models, residuals are relatively simple to examine and they have been studied extensively. However, in multivariate models and in the analysis of longitudinal data, residuals have complex structure, are correlated and do not often follow the normal distribution. In this study, we consider decomposed residuals defined using the growth curve model. We also transformed these residuals to uncorrelated residuals using Cholesky decomposition of the covariance matrix. Extensive simulations demonstrate that the decomposed and transformed residuals perform well for assessing model fit, checking the normality assumption as well as identifying outliers. We illustrate performance of our method using real data set.


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