Selected statistical methods of data analysis for multivariate functional data

By Tomasz Górecki, Miroslaw Krzysko, Łukasz Waszak, W. Wołyński. 2018

66 citations recorded by the source corpus.

Data in the form of a continuous vector function on a given interval are referred to as multivariate functional data. These data are treated as realizations of multivariate random processes. The paper is devoted to three statistical dimension reduction techniques for multivariate data. For the first one, principal components analysis, the authors present a review of a recent paper (Jacques and Preda in, Comput Stat Data Anal, 71:92–106, 2014). For two others one, canonical variables and discriminant coordinates, the authors extend existing works for univariate functional data to multivariate. These methods for multivariate functional data are presented, illustrated and discussed in the context of analyzing real data sets. Each of these techniques is applied on real data set.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/767e23f3811afc5f52855ff56497eb453b5730f4

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In Artificial Intelligence

1. Ana M. Aguilera
2. Manuel Escabias
3. Christian Acal

In Statistics and Probability

1. Ana M. Aguilera
2. Manuel Escabias
3. Christian Acal
4. Simon N. Wood

Citations in the corpus

• New Modeling Approaches Based on Varimax Rotation of Functional Principal Components (2020, background): “It is known that the greatest contribution in the structure of a functional principal component is given by the process variables associated with the greatest values of the corresponding weight curve at certain time points [20].”