Kernel density estimation for linear processes

• 成果类型: Article
• 署名作者: Wu, WB; Mielniczuk, J
• 署名单位: University of Chicago; Polish Academy of Sciences; Institute of Computer Science of the Polish Academy of Sciences
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 2002
• 页码: 1441-1459
• 关键词: noncentral limit-theorems; Empirical Process; CONVERGENCE

摘要

In this paper we provide a detailed characterization of the asymptotic behavior of kernel density estimators for one-sided linear processes. The conjecture that asymptotic normality for the kernel density estimator holds under short-range dependence is proved under minimal assumptions on bandwidths. We also depict the dichotomous and trichotomous phenomena for various choices of bandwidths when the process is long-range dependent.