Efficient classification for longitudinal data

A new classifier, QIFC, is proposed based on the quadratic inference function for longitudinal data. Our approach builds a classifier by taking advantage of modeling information between the longitudinal responses and covariates for each class, and assigns a new subject to the class with the shortest newly defined distance to the subject. For finite sample applications, this enables one to overcome the difficulty in estimating covariance matrices while still incorporating correlation into the classifier. The proposed classifier only requires the first moment condition of the model distribution, and hence is able to handle both continuous and discrete responses. Simulation studies show that QIFC outperforms competing classifiers, such as the functional data classifier, support vector machine, logistic regression, linear discriminant analysis, the naive Bayes classifier and the decision tree in various practical settings. Two time-course gene expression data sets are used to assess the performance of QIFC in applications.