Automatic classification of functional data with extremal information

Abstract

In this paper we develop statistical techniques for clustering smooth functional data based on their extremal features. Smooth functional data arise in many application fields. Our work is motivated by a problem in quality control monitoring of water supplied for human consumption, where both the level and the shape of each function are important for classification purposes.