Robust estimation for spatial models and the skill test for disease diagnosis

True positive rate;False positive rate;Classification;Disease diagnosis;Skill test;Robust estimation;Spatial models;Markov random field models;Spatial lattice data;Koziol-Green model and mean-shift model;Area under the curve;ROC curve

Lin, Shu-Chuan ; Industrial and Systems Engineering

University:Georgia Institute of Technology

Department:Industrial and Systems Engineering

关键词: True positive rate;    False positive rate;    Classification;    Disease diagnosis;    Skill test;    Robust estimation;    Spatial models;    Markov random field models;    Spatial lattice data;    Koziol-Green model and mean-shift model;    Area under the curve;    ROC curve;   

Others  :  https://smartech.gatech.edu/bitstream/1853/26681/1/lin_shuchuan_200812_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

This thesis focuses on (1) the statistical methodologies for the estimation of spatial data with outliers and (2) classification accuracy of disease diagnosis.Chapter I, Robust Estimation for Spatial Markov Random Field Models:Markov Random Field (MRF) models are useful in analyzing spatial lattice datacollected from semiconductor device fabrication and printed circuit board manufacturing processes or agricultural field trials. When outliers are present in the data, classical parameter estimation techniques (e.g., least squares) can be inefficient and potentially mislead the analyst. This chapter extends the MRF model to accommodate outliers and proposes robust parameter estimation methods such as the robust M- and RA-estimates. Asymptotic distributions of the estimates with differentiable and non-differentiable robustifying function are derived. Extensive simulation studies explore robustness properties of the proposed methods in situations with various amounts of outliers in different patterns. Also provided are studies of analysis of grid data with and without the edge information. Three data sets taken from the literature illustrate advantages of the methods.Chapter II, Extending the Skill Test for Disease Diagnosis:For diagnostic tests, we present an extension to the skill plot introduced by Mozerand Briggs (2003). The method is motivated by diagnostic measures for osteoporosis in a study. By restricting the area under the ROC curve (AUC) according to the skill statistic, we have an improved diagnostic test for practical applications by considering the misclassification costs. We also construct relationships, using the Koziol-Green model and mean-shift model, between the diseased group and the healthy group for improving the skill statistic. Asymptotic properties of the skill statistic are provided. Simulation studies compare the theoretical results and the estimates under various disease rates and misclassification costs. We apply the proposed method in classification of osteoporosis data.