Nonparametric and Robust Approach in Time Series Analysis

时间序列分析中的非参数稳健方法

• 批准号: 16540110
• 负责人: KONDO Masao
• 金额: $ 1.54万
• 依托单位: Kagoshima University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2004
• 资助国家: 日本
• 起止时间: 2004 至 2005
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-16540110/
• 关键词: time series analysis; nonparametric; robust; ノンパラメトリック法; ロバスト性

This research is concerned with the estimation of the autocorrelation of a stationary Gaussian process by nonparametric and robust method without assuming a specific parametric model, and the tests based on nonparametric spectral estimators in time series analysis.We discuss the estimation of the autocorrelation based on limiter estimating functions for a stationary Gaussian process having the representation of an infinite moving average. Several new estimators are proposed and their asymptotic distributions are obtained. The estimation of the autocorrelation of a stationary Gaussian process with additive outliers is discussed. The biases of several estimators of the autocorrelation based on limiter estimating functions are compared.We consider to test whether the integral of appropriate function of the spectral density is equal to a given constant or not. For this problem a test based on a nonparametric spectral estimator is proposed, and the asymptotic power evaluation under a sequence of nonparametric contiguous alternatives is given. We discuss to test whether the integral of appropriate function of the spectral density of a process, is equal to that of the other process, or not.

本研究涉及在不假设特定参数模型的情况下，通过非参数鲁棒方法估计平稳高斯过程的自相关性，并在时间序列分析中基于非参数谱估计器进行测试。我们讨论了基于限制器估计函数的具有无限移动平均表示的平稳高斯过程的自相关估计。提出了几种新的估计量并获得了它们的渐近分布。讨论了具有加性异常值的平稳高斯过程的自相关估计。比较了基于限制器估计函数的几种自相关估计器的偏差。我们考虑测试谱密度的适当函数的积分是否等于给定常数。针对该问题，提出了基于非参数谱估计器的测试，并给出了一系列非参数连续替代方案下的渐近功效评估。我们讨论测试一个过程的谱密度的适当函数的积分是否等于另一个过程的积分。

项目成果

Asymptotic properties of linear combinations of U-statistics with degenerate kernels

U 统计量与简并核的线性组合的渐近性质

• 发表时间: 2005
• 期刊: Journal of Nonparametric Statistics 17・2
• 作者: M.Fukushima;H.Tanaka;Hajime Yamato
• 通讯作者: Hajime Yamato