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

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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.