Multiple-response repeated measurement or multivariate growth curve model with distribution-free errors

ABSTRACT

In this article, we propose a new modeling approach for the multivariate growth curve model with distribution-free errors, which is a useful tool for analyzing multiple-response repeated measurements. We first use the outer product least-squares technique to directly estimate covariance and then explore the feasible generalized least-squares technique to derive the estimator of regression coefficients. Large-sample properties are investigated for these estimators. Moreover, the above estimations for covariance and regression coefficients are extended to the situation under certain null hypothesis tests and the best subset BIC is used for variable selection. A real dataset is analyzed to demonstrate the usefulness and competency of the proposed methodology for model specification (identification) and model fitting (parameter estimation) in multiple-response repeated measurements.

KEYWORDS:

• Asymptotic normality
• Feasible generalized least squares
• Multivariate growth curve model
• Outer product
• Repeated measurement.

MATHEMATICS SUBJECT CLASSIFICATION:

• Primary 62H12
• Secondary 62F12
• 62H10