Mathematical Sciences: Statistical Procedures for Errors-in-Variables Regression and Goodness of Fit

这项研究将集中在统计理论的主题上，并将致力于在线性变量误差回归模型和拟合优度程序领域开发新的程序。这两个项目都是在统计和概率的一般领域。变量误差回归模型描述了当解释变量和响应变量都存在随机误差时变量之间的关系。本研究的重点将放在测量的可靠性在确定变量误差回归技术的性能中的作用。拟合优度程序评估分布假设或结构模型的充分性。这项研究将涉及一般的基于多项式的程序的研究，以测试拟合在分类数据。

This research will focus on topics in statistical theory and will be aimed at developing new procedures in the areas of linear errors-in-variables regression models and goodness of fit procedures. Both projects are in the general area of statistics and probability. Errors-in-variables regression models describe relationships among variables when both the explanatory and response variables are subject to random error. Emphasis in this research will be placed on the role of the reliability of measurements in determining the performance of errors-in- variables regression techniques. Goodness of fit procedures assess the adequacy of distributional assumptions or of structural models. This research will involve a study of general multinomial-based procedures for testing fit in categorical data.