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和 将致力于在线性领域开发新的程序， 变量误差回归模型和拟合优度 程序. 这两个项目都属于一般统计领域， 概率 变量误差回归模型描述了 变量之间的关系，当解释和 响应变量受随机误差的影响。 在此强调 研究将放在可靠性的作用， 在确定误差的性能测量， 变量回归技术 拟合优度程序 评估分配假设的充分性或 结构模型 这项研究将涉及一项研究， 用于检验分类数据中拟合的基于多项式的程序。

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.