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PubMed · 11809320

Multivariate cubic spline smoothing in multiple prediction.

Harry Khamis·Michael Kepler
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Abstract

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.

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Computer SimulationHumansLongitudinal StudiesModels, StatisticalMultivariate AnalysisPredictive Value of TestsProgramming LanguagesSoftwareJournal Article

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Harry Khamis, Michael Kepler. 2002. Multivariate cubic spline smoothing in multiple prediction.. https://doi.org/10.1016/s0169-2607(01)00114-6

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