This paper studies the kernel-type distribution estimator based on asymptotically almost negatively associated(AANA,for short)samples.The rate of uniformly strong consistencyis established under some mild conditions.A...This paper studies the kernel-type distribution estimator based on asymptotically almost negatively associated(AANA,for short)samples.The rate of uniformly strong consistencyis established under some mild conditions.As applications,the uniformly strong convergence rates of kernel-type density estimator and kernel-type hazard rate estimator are also obtained.Some Monte Carlo simulations are presented to illustrate the finite sample performance of the kernel method.Finally,a real data analysis of Alibaba stock returns data is used to illustrate the usefulness of the proposed methodology.展开更多
基金supported by National Natural Science Foundation of China[grant number 11671012]Natural Science Foundation of Anhui Province[grant numbers 2008085MA14 and 2108085MA06]+1 种基金Scientific and Technological Innovation Project of Higher Education Institutions in Shanxi Province[grant number 23L165]General Projects under the 2023 Annual Research Plan of the China Society for Business Statistics[grant number 2023STY62].
文摘This paper studies the kernel-type distribution estimator based on asymptotically almost negatively associated(AANA,for short)samples.The rate of uniformly strong consistencyis established under some mild conditions.As applications,the uniformly strong convergence rates of kernel-type density estimator and kernel-type hazard rate estimator are also obtained.Some Monte Carlo simulations are presented to illustrate the finite sample performance of the kernel method.Finally,a real data analysis of Alibaba stock returns data is used to illustrate the usefulness of the proposed methodology.
