TY - JOUR
T1 - Homogeneity tests for functional data based on depth-depth plots with chemical applications
AU - Calle-Saldarriaga, Alejandro
AU - Laniado, Henry
AU - Zuluaga, Francisco
AU - Leiva, Víctor
N1 - Funding Information:
The authors would like to thank the Editors and Reviewers for their constructive comments on an earlier version of this manuscript which resulted in this improved version. The research of A. Calle-Saldarriaga, H. Laniado, and F. Zuluaga was funded by the Internal Project ?Statistical homogeneity test for infinite dimensional data? with number 881-000044 from Universidad EAFIT, Medell?n, Colombia. The research of V. Leiva was partially funded by FONDECYT, project grant number 1200525 from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science and Technology, Knowledge and Innovation.
Funding Information:
The authors would like to thank the Editors and Reviewers for their constructive comments on an earlier version of this manuscript which resulted in this improved version. The research of A. Calle-Saldarriaga, H. Laniado, and F. Zuluaga was funded by the Internal Project “Statistical homogeneity test for infinite dimensional data” with number 881-000044 from Universidad EAFIT, Medellín, Colombia . The research of V. Leiva was partially funded by FONDECYT , project grant number 1200525 from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science and Technology, Knowledge and Innovation .
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12/15
Y1 - 2021/12/15
N2 - 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.
AB - 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.
KW - Bootstrapping
KW - Data science
KW - DD plots
KW - Hypothesis testing
KW - Nonparametric statistics
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=85118338067&partnerID=8YFLogxK
U2 - 10.1016/j.chemolab.2021.104420
DO - 10.1016/j.chemolab.2021.104420
M3 - Article
AN - SCOPUS:85118338067
VL - 219
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
SN - 0169-7439
M1 - 104420
ER -