Mathematical Sciences: Statistical Inference for Some Dynamic Phenomena

数学科学：一些动态现象的统计推断

• 批准号: 8900613
• 负责人: David Brillinger
• 金额: $ 16.36万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-06-01 至 1992-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8900613&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Statistical; Inference; Some

The principal investigator will develop stochastic models and statistical methods for handling data recorded on phenomena evolving in time and/or distributed in space. Because of the complexity of the data, the subject matter of the substantive field will play a crucial role in modelling and analysis; however it is anticipated that the techniques developed will be broadly applicable. Variants of techniques for ordinary random variables will be developed for complex random functions. Some of the particular problems to be studied are: 1) parameter estimation and input signal design for the bilinear system basic to nuclear magnetic resonance spectroscopy, 2) technique development for modelling and discerning the structure of collections of interacting point processes as applicable to networks of firing nerve cells. The investigator will obtain both theoretical and applied results for the problems studied. The principal investigator will develop stochastic model and statistical methods for handling data recorded on phenomena evolving in time and/or distributed in space. Because of the complexity of the data, the subject matter of the substantive field will play a crucial role in modelling and analysis; however it is anticipated that the techniques developed will be broadly applicable. Variants of techniques for ordinary random variables will be developed for complex random functions. Some of the particular problems to be studied are: 1) parameter estimation and input signal design for the bilinear system basic to nuclear magnetic resonance spectroscopy, 2) technique development for modelling and discerning the structure of collections of interacting point processes as applicable to networks of firing nerve cells. The investigator will obtain both theoretical and applied results for the problems studied.

首席研究员将开发随机模型 处理现象记录数据的统计方法 在时间上演化和/或在空间上分布。 因为 数据的复杂性，实质性的主题， 场将在建模和分析中发挥关键作用;然而， 预计所开发的技术将广泛地 适用因 普通随机变量技术的变体 将为复杂的随机函数开发。 一些 具体研究的问题有：1）参数估计 核双线性系统的输入信号设计 磁共振波谱学，2）技术开发， 建模和识别的集合的结构 适用于点火网络的相互作用点过程 神经细胞 研究者将获得理论和 所研究问题的应用结果。 主要研究者将开发随机模型， 处理现象记录数据的统计方法 在时间上演化和/或在空间上分布。 因为 数据的复杂性，实质性的主题， 场将在建模和分析中发挥关键作用;然而， 预计所开发的技术将广泛地 适用因 普通随机变量技术的变体 将为复杂的随机函数开发。 一些 具体研究的问题有：1）参数估计 核双线性系统的输入信号设计 磁共振波谱学，2）技术开发， 建模和识别的集合的结构 适用于点火网络的相互作用点过程 神经细胞 研究者将获得理论和 所研究问题的应用结果。