TY - JOUR
T1 - A faster U-statistic for testing independence in the functional linear models
AU - Zhao, Fanrong
AU - Lin, Nan
AU - Hu, Wenjuan
AU - Zhang, Baoxue
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/3
Y1 - 2022/3
N2 - Testing the dependence between the response and the functional predictor in a functional linear model is of fundamental importance. In this paper, based on a U-statistic of order two, we develop a computationally more efficient test for lacking of dependence in functional linear regression model. By the martingale central limit theorem, we prove that the asymptotic normality of the proposed test statistic under some mild regularity conditions. Simulation results show that our proposed test can be tens or hundreds time faster than the FLUTE test by Hu et al. (2020) which uses a U-statistic of order four. We further demonstrate the superiority of our test by two real data applications.
AB - Testing the dependence between the response and the functional predictor in a functional linear model is of fundamental importance. In this paper, based on a U-statistic of order two, we develop a computationally more efficient test for lacking of dependence in functional linear regression model. By the martingale central limit theorem, we prove that the asymptotic normality of the proposed test statistic under some mild regularity conditions. Simulation results show that our proposed test can be tens or hundreds time faster than the FLUTE test by Hu et al. (2020) which uses a U-statistic of order four. We further demonstrate the superiority of our test by two real data applications.
KW - Asymptotic normality
KW - Functional linear model
KW - Nonparametric
KW - U-statistic
UR - https://www.scopus.com/pages/publications/85114269048
U2 - 10.1016/j.jspi.2021.08.002
DO - 10.1016/j.jspi.2021.08.002
M3 - Article
AN - SCOPUS:85114269048
SN - 0378-3758
VL - 217
SP - 188
EP - 203
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
ER -