"Nonparametric Regression Methods For Nonlinear Time Series Models"

“非线性时间序列模型的非参数回归方法”

• 批准号: 0805748
• 负责人: Michael Levine
• 金额: $ 10万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0805748&HistoricalAwards=false
• 关键词: Nonparametric; Regression; Methods; Nonlinear; Time

The aim of this project is to develop the methodology for estimation and testing of the nonlinear time series models involving a large number of predictor variables. The key idea is to adapt a number of techniques that are used in the nonparametric regression theory and that can be, after suitable justification, transferred to the time series setting. In addition to the above, the decision-theoretic properties of the resulting estimators are established for the first time. This is achieved by establishing asymptotic equivalence results between the nonparametric regression and various nonlinear time series models. The models studied in this project are very commonly encountered in different areas of application. Many of these are the areas of federal strategic interest. As an example, one can mention forecasting the levels of future flooding and predicting the volume of production in many areas of industry. Another very important area of application is building the models that explain long-term changes in sea surface temperature and, by doing so, help explain and predict the future changes in the global climate. All of the above models often include a lot of predictor variables and this makes the choice of the model needed very difficult in practice. Based on the methods and tests proposed in this project, efficient model selection procedures can be enacted that can greatly help in choosing the right model. After the right model is chosen, high quality forecasts can be obtained that are not only of interest for researchers in science, but that also benefit society as a whole.

该项目的目的是开发涉及大量预测变量的非线性时间序列模型估算和测试的方法。关键思想是调整非参数回归理论中使用的许多技术，并且在适当的理由之后，可以转移到时间序列设置后。除上述内容外，首次建立了结果估计器的决策理论属性。这是通过在非参数回归与各种非线性时间序列模型之间建立渐近等价结果来实现的。该项目中研究的模型通常在不同的应用领域遇到。其中许多是联邦战略利益的领域。例如，可以提及预测未来洪水的水平，并预测许多行业领域的生产量。应用的另一个非常重要的领域是构建模型，这些模型可以解释海面温度的长期变化，并通过这样做，有助于解释和预测全球气候的未来变化。以上所有模型通常都包含许多预测变量，这使得在实践中非常困难的模型选择。根据该项目中提出的方法和测试，可以制定有效的模型选择程序，从而极大地有助于选择正确的模型。选择正确的模型后，可以获得高质量的预测，不仅是科学研究人员感兴趣的，而且还会使整个社会受益。