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

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

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.