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Studies of Possibilities and Limits of Statistical Prediction

统计预测的可能性和局限性的研究

基本信息

批准号：
11308010
负责人：
SATO Yoshiharu
金额：
$ 19.71万
依托单位：
Hokkaido University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (A)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2002
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11308010/
关键词：
Data Science Statistical Model Statistical Inference Model Selection Robustness of Models Forecasting Data Mining

项目摘要

The aim of this project was to study out the possibilities and limits of statistical prediction in the practical problems. There are two situations in the statistical prediction. One is an interpolation and the other is an extrapolation. The typical concept of the former, interpolation, is a theory of regression, namely, the nonlinear, semi-parametric and nonparametric regression. Moreover, the several modeling techniques by which the reasonable and useful information are extracted have been included. On these methods, we get excellent results, for instance, "growth curve model with hierarchcal within-individual design matrices", "echelon analysis of the relationship between population and land cover patterns based on remote sensing data", "an algorithm with projection pursuit for sliced inverse regression model", "Kullback-Leibler information consistent estimator for censored data" and so on. On the other hand, the latter concept, extrapolation, is familiar to weather or a business or a stock price forecasting. The typical method is a time series analysis. Although, there still remain many dfficulties, in this project, many useful results are proposed, especially on the state space modeling a relaxation of error distribution and several filtering theory for the noise reduction. For example, "computational implementations of nonlinear non-Gaussian prediction and filtering formulas", "a combining forecast method using a probabilistic neural network", "forecasting structural changes in a state-space model", "extension of the MDL criterion by shrinkage method" and so on.

本项目的目的是研究统计预测在实际问题中的可能性和局限性。在统计预测中有两种情况。一个是插值，另一个是外推。前者的典型概念是插值，它是一种回归理论，即非线性、半参数和非参数回归。此外，还介绍了提取合理有用信息的几种建模技术。在这些方法中，我们得到了很好的结果，如“层次内个体设计矩阵的生长曲线模型”、“基于遥感数据的人口与土地覆盖关系的阶梯分析”、“切片逆回归模型的投影寻踪算法”、“截尾数据的Kullback-Leibler信息一致估计量”等。外推，是熟悉的天气或企业或股票价格预测。典型的方法是时间序列分析。虽然仍有许多困难，但在本项目中，提出了许多有用的结果，特别是在状态空间建模、误差分布的松弛和几种滤波降噪理论方面。例如，“非线性非高斯预测和滤波公式的计算实现”，“使用概率神经网络的组合预测方法”，“预测状态空间模型中的结构变化”，“通过收缩方法扩展MDL准则”等。