CMStatistics 2021: Start Registration
View Submission - CMStatistics
Title: Flexible integrated functional depths Authors:  Germain Van Bever - Universite de Namur (Belgium) [presenting]
Stanislav Nagy - Charles University (Czech Republic)
Pauliina Ilmonen - Aalto University School of Science (Finland)
Sami Helander - Aalto University School of Science (Finland)
Lauri Viitasaari - Aalto University (Finland)
Abstract: A new class of functional depths is introduced. A generic member of this class is coined Jth order kth moment integrated depth. It is based on the distribution of the cross-sectional halfspace depth of a function in the marginal evaluations (in time) of the random process. Asymptotic properties of the proposed depths are provided: we show that they are uniformly consistent and satisfy an inequality related to the law of the iterated logarithm. Moreover, limiting distributions are derived under mild local regularity assumptions. The versatility displayed by the new class of depths makes them particularly amenable for capturing important features of functional distributions. This is illustrated in supervised learning, where we show that the corresponding maximum depth classifiers outperform classical competitors.