TITLE:
Partial Functional Linear Models with ARCH Errors
AUTHORS:
Yafei Wang, Tianfa Xie, Zhongzhan Zhang
KEYWORDS:
Asymptotic Normality, ARCH(p) Errors, Functional Principal Components, Convergence Rate, Least Absolute Deviation
JOURNAL NAME:
Open Journal of Statistics,
Vol.8 No.2,
April
26,
2018
ABSTRACT: In this paper, the estimation of the parameters in partial functional
linear models with ARCH(p) errors is discussed. With employing the functional
principle component, a hybrid estimating method is suggested. The asymptotic
normality of the proposed estimators for both the linear parameter in the mean
model and the parameter in the ARCH error model is obtained, and the
convergence rate of the slope function estimate is established. Besides, some simulations and a real data analysis are conducted
for illustration, and it is shown that the proposed method performs well with a
finite sample.