We present some asymptotic results on the distance between the means of samples of curves generated by independent continuous time stochastic processes in L2(T). The asymptotic results are based on mild assumptions on the moments of the processes, and there are no conditions on their probability distribution. The metrics we consider extends the Mahalanobis istance to L2(T) without any truncation on the first principal components. Applications in the context of classification of functional data are finally discussed.

Ghiglietti, A., Ieva, F., Maria Paganoni, A. (2017). A generalized Mahalanobis distance for the classification of functional data. In CLADAG2017. Book of Short Papers. Mantova : Universitas Studiorum.

A generalized Mahalanobis distance for the classification of functional data

Andrea Ghiglietti;
2017

Abstract

We present some asymptotic results on the distance between the means of samples of curves generated by independent continuous time stochastic processes in L2(T). The asymptotic results are based on mild assumptions on the moments of the processes, and there are no conditions on their probability distribution. The metrics we consider extends the Mahalanobis istance to L2(T) without any truncation on the first principal components. Applications in the context of classification of functional data are finally discussed.
No
paper
Scientifica
Functiona Data; Distances in L2; Two-sample problems
English
Classification and Data Analysis Group 2017
978-88-99459-71-0
Ghiglietti, A., Ieva, F., Maria Paganoni, A. (2017). A generalized Mahalanobis distance for the classification of functional data. In CLADAG2017. Book of Short Papers. Mantova : Universitas Studiorum.
Ghiglietti, A; Ieva, F; Maria Paganoni, A
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/391743
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