Multivariate Cubic Spline Smoothing in Multiple Prediction
Given longitudinal data for several variables, including a given outcome variable, it is desired to predict the outcome for a specific individual, or more generally experimental unit, in such a way that the predicted value is both accurate and resistant (i.e. has good cross-validation). There are certain data-analytic difficulties associated with long-term multivariate longitudinal data that must be overcome in the prediction process. This paper provides a program written in the Statistical Analysis System (SAS) programming language, based generally on the Roche-Wainer-Thissen stature prediction model, that enables the researcher to overcome these difficulties. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.
Khamis, H. J.,
& Kepler, M.
(2002). Multivariate Cubic Spline Smoothing in Multiple Prediction. Computer Methods and Programs in Biomedicine, 67 (2), 131-136.