The Mahalanobis Distance for Functional Data With Applications to Classification Articles uri icon

publication date

  • April 2015

start page

  • 281

end page

  • 291

issue

  • 2

volume

  • 57

international standard serial number (ISSN)

  • 0040-1706

electronic international standard serial number (EISSN)

  • 1537-2723

abstract

  • This article presents a new semidistance for functional observations that generalizes the Mahalanobis distance for multivariate datasets. The main characteristics of the functional Mahalanobis semidistance are shown. To illustrate the applicability of this measure of proximity between functional observations, new versions of several well-known functional classification procedures are developed using the functional Mahalanobis semidistance. A Monte Carlo study and the analysis of two real examples indicate that the classification methods used in conjunction with the functional Mahalanobis semidistance give better results than other well-known functional classification procedures. This article has supplementary material online.