TY - JOUR
T1 - Functional additive expectile regression in the reproducing kernel Hilbert space
AU - Liu, Yuzi
AU - Peng, Ling
AU - Liu, Qing
AU - Lian, Heng
AU - Liu, Xiaohui
PY - 2023/11
Y1 - 2023/11
N2 - In the literature, the functional additive regression model has received much attention. Most current studies, however, only estimate the mean function, which may not adequately capture the heteroscedasticity and/or asymmetries of the model errors. In light of this, we extend functional additive regression models to their expectile counterparts and obtain an upper bound on the convergence rate of its regularized estimator under mild conditions. To demonstrate its finite sample performance, a few simulation experiments and a real data example are provided. © 2023 Elsevier Inc.
AB - In the literature, the functional additive regression model has received much attention. Most current studies, however, only estimate the mean function, which may not adequately capture the heteroscedasticity and/or asymmetries of the model errors. In light of this, we extend functional additive regression models to their expectile counterparts and obtain an upper bound on the convergence rate of its regularized estimator under mild conditions. To demonstrate its finite sample performance, a few simulation experiments and a real data example are provided. © 2023 Elsevier Inc.
KW - Convergence rate
KW - Functional additive expectile regression
KW - Reproducing kernel Hilbert space
KW - Upper bound
UR - http://www.scopus.com/inward/record.url?scp=85166539930&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85166539930&origin=recordpage
U2 - 10.1016/j.jmva.2023.105214
DO - 10.1016/j.jmva.2023.105214
M3 - RGC 21 - Publication in refereed journal
SN - 0047-259X
VL - 198
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
M1 - 105214
ER -