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State-space model approach to longitudinal data analysis by bootstrap

通过 Bootstrap 进行纵向数据分析的状态空间模型方法

基本信息

批准号：
10480050
负责人：
TAGURI Masaaki
金额：
$ 4.03万
依托单位：
Chiba University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B).
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-10480050/
关键词：
Wind velocity data by artificial satellite Test of curve difference Bootstrap test Moving block AR model Area difference statistic Size and power of tests Simulation ブートストラップ検定状態空間モデル moving block bootstrap法 2種類の曲線の差の検定検出力のシミュレーシ

项目摘要

The dataset used in this research is the measurements of wind velocity obtained from an artificial satellite (HRDI), and a rader on the earth, ranging from 80km to 90km at every 1km for 14 days. The interest is to examine the significant difference between these two measuring mechanisms. Considering the dependency of the data, we investigated the statistical testing problem by the following procedure : (1) Based on miscellaneous preliminary data analyses, three kinds of state space models were constructed. (2) Smoothing curves, obtained by the Kalman filter technique, were regarded as an initial sample. (3) Applying a modified moving block method, bootstrap samples were constructed. (4) By mixing the two samples, we made bootstrap samples taken from the null hypothesis. (5) The AUC statistic, which represents the area surrounded by the two smoothing curves, and then the bootstrap distribution were computed, and the statistical testing was carried out. As the result, the following findings were obtained : The wind velocity was affected by measuring altitude and day factors. Also the measurements by HRDI overestimated the true wind velocity. The estimated curves were considered as reasonable, considering the weak wind velocity zone at 90-100km. Next we generalized the problem, and tested the significant difference between two non-stationary dataset (two curves). Eight kinds of testing procedures were proposed, and were compared with some traditional methods. Through simulation sudies based on the wind velocity data, the sizes and powers were examined and compared with each other. As for test statistics, AUC was mainly investigated, however the squared and absolute difference statistics were also taken into consideration. The effect of scale adjustment was also examined. As the result, the proposed bootstrap test using AUC was found to be superior to the traditional methods from the viewpoints of sizes and powers in almost cases.

本研究所用的数据集是由一颗人造卫星（HRDI）和一台地面雷达获得的风速测量数据，范围为80 - 90 km，每隔1 km，持续14天。我们的兴趣是研究这两种测量机制之间的显着差异。考虑到数据的相关性，我们采用以下步骤研究了统计检验问题：（1）在各种初步数据分析的基础上，建立了三种状态空间模型。(2)平滑曲线，通过卡尔曼滤波技术获得，被视为一个初始样本。(3)应用一种改进的移动块法，自助样本的构建。(4)通过混合两个样本，我们从零假设中提取了自助样本。(5)计算AUC统计量（代表两条平滑曲线包围的面积）和bootstrap分布，并进行统计学检验。结果表明：风速受测风高度和日因子的影响。HRDI的测量也高估了真实的风速。考虑到90- 100 km处的弱风速区，认为估算曲线是合理的。接下来，我们推广了这个问题，并测试了两个非平稳数据集（两条曲线）之间的显著差异。提出了8种检测方法，并与传统方法进行了比较。通过对风速数据的模拟研究，对不同风速下的风场大小和功率进行了比较。至于检验统计量，主要研究AUC，但也考虑了平方和绝对差统计量。还审查了比额表调整的影响。结果发现，从大小和功效的角度来看，在大多数情况下，使用AUC的Bootstrap检验上级传统方法。