Mathematical Sciences: Strong Approximations of the Distributions of Estimators in Linear Stochastic Models

数学科学：线性随机模型中估计量分布的强近似

• 批准号: 8801992
• 负责人: Paramaraj Jeganathan
• 金额: $ 5.55万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-07-01 至 1990-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8801992&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Strong; Approximations; Distributions

The research for this project is directed at establishing suitable approximations to the distributions of estimators of parameters in linear stochastic models. By approximations, Berry.Essen bounds and asymptotic expansions are also included. The method that will be used will be different from the usual method of characteristic functions and will also give approximations uniformly over all Borel subsets, which is also one of the aims of the project. Some specific statistical models that will be considered in detail include nonlinear regression, autoregression, regression with lagged dependent variables. Robust versions of the estimators in these models will be considered in addition to the least squares estimators.

该项目的研究旨在建立 估计量分布的适当近似 线性随机模型中的参数。 通过近似， Berry.埃森界和渐近展开式也包括在内。 将使用的方法将不同于通常的方法 方法的特征函数，并将给出 在所有Borel子集上均匀近似，这也是 该项目的目标之一。 将考虑的一些特定统计模型 详细包括非线性回归，自回归，回归 滞后的因变量 强大版本的 这些模型中的估计量将被视为除了 最小二乘估计