Discriminant Analysis of Distributional Data viaFractional Programming
Methodology
2020-10-15 v1
Abstract
We address classification of distributional data, where units are described by histogram or interval-valued variables. The proposed approach uses a linear discriminant function where distributions or intervals are represented by quantile functions, under specific assumptions. This discriminant function allows defining a score for each unit, in the form of a quantile function, which is used to classify the units in two a priori groups, using the Mallows distance. There is a diversity of application areas for the proposed linear discriminant method. In this work we classify the airline companies operating in NY airports based on air time and arrival/departure delays, using a full year fights.
Keywords
Cite
@article{arxiv.2010.06941,
title = {Discriminant Analysis of Distributional Data viaFractional Programming},
author = {S. Dias and P. Brito and P. Amaral},
journal= {arXiv preprint arXiv:2010.06941},
year = {2020}
}