Nonparametric Functional Central Limit Theorem for Time Series Regression with Application to Self-normalized Confidence Interval.
This paper is concerned with the inference of nonparametric mean function in a time series context. The commonly used kernel smoothing estimate is asymptotically normal and the traditional inference procedure then consistently estimates the asymptotic variance function and relies upon normal approximation. Consistent estimation of the asymptotic variance function involves another level of nonparametric smoothing. In practice, the choice of the extra bandwidth parameter can be difficult, the inference results can be sensitive to bandwidth selection and the normal approximation can be quite unsatisfactory in small samples leading to poor coverage. To alleviate the problem, we propose to extend the recently developed self-normalized approach, which is a bandwidth free inference procedure developed for parametric inference, to construct point-wise confidence interval for nonparametric mean function. To justify asymptotic validity of the self-normalized approach, we establish a functional central limit theorem for recursive nonparametric mean regression function estimates under primitive conditions and show that the limiting process is a Gaussian process with non-stationary and dependent increments. The superior finite sample performance of the new approach is demonstrated through simulation studies.
Department of Statistics, Miami University, 311 Upham Hall, Oxford, OH 45056.
Seonjin
S
Kim
Department of Statistics, Penn State University, 326 Thomas, University Park, PA 16802.
Zhibiao
Z
Zhao
Department of Statistics, University of Illinois, 725 South Wright Street, Urbana, IL 61801.
Xiaofeng
X
Shao
Conditional heteroscedasticity; Functional central limit theorem; Nonparametric regression; Self-normalization; Time series