Mathematical Sciences: Statistical Inference for Time SeriesModels

主要研究者将调查三个方面， 时间序列模型的统计推断：迭代方法 不完全相依数据的非线性时间估计 系列分析，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 调查员关于灵活阈值类别工作 自回归模型，其中稳定性 和遍历性的一些时间序列模型将被研究。 不完全相依数据的估计工作将 涉及算法开发。 首席研究员正在研究各种问题， 时间序列分析 时间序列分析用于建模 数据是连续的和相关的。 电流 感兴趣的是开发一类模型， 真实的数据中的线性。 另一个调查领域将 涉及数据不完整情况。 这再次 表示可能发生在真实的数据中的情况。

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.