Homogeneity tests for functional data based on depth-depth plots with chemical applications

Alejandro Calle-Saldarriaga, Henry Laniado, Francisco Zuluaga, Víctor Leiva

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

One of the standard problems in statistics is determining if two samples come from the same population, that is, testing homogeneity for two samples. In this paper, we propose homogeneity tests in the context of functional data, adopting an idea from multivariate analysis corresponding to the depth-depth plot. This plot is a multivariate generalization of the quantile-quantile plot. We propose some statistics based on the depth-depth plot, and use bootstrapping to approximate their null distributions. We conduct simulations to state the empirical size and power of the proposed tests, obtaining better results than other homogeneity tests considered in the literature. We detect that our test has very high power in relation to other competing tests. We employ many different depths based on what is proposed in the literature to see which is more suitable for this kind of homogeneity testing. Finally, we illustrate the obtained results with chemical heterogeneous data to show potential applications, getting consistent results.

Original languageEnglish
Article number104420
JournalChemometrics and Intelligent Laboratory Systems
Volume219
DOIs
StatePublished - 15 Dec 2021
Externally publishedYes

Keywords

  • Bootstrapping
  • Data science
  • DD plots
  • Hypothesis testing
  • Nonparametric statistics
  • Robustness

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