A statistical model of tracking

A method is proposed to model individual patterns of growth over time by linear combinations of optimally chosen weighted orthogonal vectors. The goal is to distinguish individuals who track from nontrackers. Nontrackers are defined as those who follow different, usually more complex, growth patterns than trackers. Thus, nontrackers require more vectors than do trackers in modeling their longitudinal observations. A method of specifying the class-specific vectors and individual weights is demonstrated. When the proportion of nontrackers in the population is small, a modified form of the Akaike maximum entropy criterion is used to select the number of vectors appopriate for each person and also to classify each person into a tracking category. When the proportion of nontrackers is large, the modified Akaike criterion together with scatterplots of the growth curve weights are needed to distinguish trackers from nontrackers. The apprach is illustrated with longitudinal observations of height measured in an epidemiologic survey of children.