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Clustering for Sparsely Sampled Functional Data
2003
Journal of the American Statistical Association
We develop a flexible model-based procedure for clustering functional data. The technique can be applied to all types of curve data but is particularly useful when individuals are observed at a sparse set of time points. In addition to producing final cluster assignments, the procedure generates predictions and confidence intervals for missing portions of curves. Our approach also provides many useful tools for evaluating the resulting models. Clustering can be assessed visually via low
doi:10.1198/016214503000189
fatcat:eljfz2g2sngu7kcuikbtleusue