The relationship between the speed in changing statistical structure and the accuracy in estimation of nonstationary spectrum

统计结构变化速度与非平稳谱估计精度的关系

• 批准号: 15500178
• 负责人: HOKIMOTO Tsukasa
• 金额: $ 0.58万
• 依托单位: HOKKAIDO UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2003
• 资助国家: 日本
• 起止时间: 2003 至 2004
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15500178/
• 关键词: structural change; nonstationarity; nonstationary spectrum; space-time model; forecasting; 非定常スペクトル密度関数; 発展型スペクトル密度関数; 局所定常性

In this research project, I have treated the statistical consideration on the inference of spectral structure of the nonstationary time series, whose statistical share changes gradually over time. The project has been proceeded with the goals that 1)mathematical evaluation on the relationship between the speed in changing statistical structure and the accuracy in estimating nonstationary spectrem, and 2)the application of the concept of (1) to the aspect of data analysisIn this project, under the assumption that the nonstationary time series follows an oscillatory process and it has a nonstationary spectral density structure, some mathematical results on the errors in estimating the nonstationary spectrum were obtained, and they were presented in the conference of ISI (International Statistical Institute), in Apr.,2005. The presented method has the disadvantage, however, that it is not necessarily flexible for the practical application. So, we also developed a new methodology, which is available for the application in the practical aspect. In this project, as a case, we developed a space-time model based on the space-time data, including different speeds in changing structure, measured by an observation satellite. Furthermore, we could show that the statistical structure we proposed is valid to some extent, via evaluation of forecasting performance by applying this model. The paper has been published in IEEE Geoscience and Remote Sensing Letters.

在这个研究项目中，我已经处理了非平稳时间序列，其统计份额随着时间的推移逐渐变化的频谱结构的推断的统计考虑。本课题的目标是：1）对统计结构变化速度与非平稳谱估计精度之间的关系进行数学评价; 2）将（1）的概念应用于数据分析方面。在ISI（International Statistical Institute）会议上，2005.然而，所提出的方法的缺点是，它不一定是灵活的实际应用。因此，我们也开发了一种新的方法，这是在实际方面的应用。在这个项目中，作为一个案例，我们开发了一个时空模型的基础上的时空数据，包括不同的速度变化的结构，由观测卫星测量。此外，我们可以表明，我们提出的统计结构是有效的，在一定程度上，通过评估的预测性能，应用该模型。该论文发表在IEEE Geoscience and Remote Sensing Letters上。

项目成果

Predicting the Spatiotemporal Chlorophyl1-a Distribution in the Sea of Japan Based on SeaFIFS Ocean Color Satellite Data

基于SeaFIFS海洋颜色卫星数据预测日本海叶绿素1-a时空分布

• 发表时间: 2006
• 期刊: IEEE Geoscience and Remote Sensing Letters Vol.3, Issuel(Forthcoming)
• 作者: Kiyofuji et al.

Prediction of Phytoplankton Concentration Based on a Simplified Space-time AR model

基于简化时空AR模型的浮游植物浓度预测

• 发表时间: 2005
• 期刊: Contributed Papers, 55^<th> Session of International Statistical Institute, Australia
• 作者: Kiyofuji et al.;Kiyofuji et al.;Hokimoto et al.
• 通讯作者: Hokimoto et al.

局所性をもつSTARモデルに基く海中のクロロフィル濃度分布の予測

基于局部STAR模型的海洋叶绿素浓度分布预测

• 发表时间: 2004
• 期刊: 科学研究費研究集会「時空間統計解析の理論と応用」
• 作者: Kiyofuji et al.;Kiyofuji et al.;Hokimoto et al.;T.Hokimoto et al.;T.Hokimoto et al.;甫喜本 司他
• 通讯作者: 甫喜本 司他

Predictability on spatiotemporal distribution of chlorophyll in the Sea of Japan

日本海叶绿素时空分布的可预测性

• 发表时间: 2004
• 期刊: Workshop on Prediction for marine resources, The Institute of Statistical Mathematics
• 作者: Kiyofuji et al.;Kiyofuji et al.;Hokimoto et al.;T.Hokimoto et al.;T.Hokimoto et al.;甫喜本 司他;T.Hokimoto et al.
• 通讯作者: T.Hokimoto et al.

T.Hokimoto: "Fitting a Nonstationary AR model to a nonstationary AR process"Bulletin of the International Statistical Institute. Book 1. 500-501 (2003)

T.Hokimoto：“将非平稳 AR 模型拟合到非平稳 AR 过程”国际统计研究所通报。