Mathematical Sciences: Statistical Inference for Time SeriesModels

数学科学：时间序列模型的统计推断

• 批准号: 9006464
• 负责人: Kung-Sik Chan
• 金额: $ 0.6万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-07-15 至 1991-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9006464&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Statistical; Inference; Time

The principal investigator will investigate three aspects of statistical inference for time series models: iterative methods of estimation for incomplete dependent data, non-linear time series analysis, forecasting with a possibly misspecified model. The non-linear time series analysis represents a continuation of the investigator's work on a flexible class of threshold autoregressive models, where the relationship between stability and ergodicity of some time series models will be investigated. The work on estimation for incomplete dependent data will involve algorithm development. The principal investigator is working on various problems in time series analysis. Time series analysis is used to model situations were data are sequential and dependent. Current interest is to develope a class of models that account for non- linearities in real data. Another area of investigation will involve situations were data are incomplete. This again represents a situation that can occur in real data.

主要研究者将研究时间序列模型统计推断的三个方面：不完整相关数据的迭代估计方法、非线性时间序列分析、使用可能错误指定的模型进行预测。 非线性时间序列分析代表了研究者对灵活的阈值自回归模型类别工作的延续，其中将研究一些时间序列模型的稳定性和遍历性之间的关系。 不完整相关数据的估计工作将涉及算法开发。 首席研究员正在研究时间序列分析中的各种问题。 时间序列分析用于对数据连续且相关的情况进行建模。 目前的兴趣是开发一类解释实际数据非线性的模型。 调查的另一个领域将涉及数据不完整的情况。 这再次代表了真实数据中可能发生的情况。