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

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dc.contributor.advisor Lu, Jye-Chyi
dc.contributor.advisor Kvam, Paul
dc.contributor.author Lin, Shu-Chuan en_US
dc.contributor.committeeMember Mei, Yajun
dc.contributor.committeeMember Serban, Nicoleta
dc.contributor.committeeMember Vidakovic, Brani
dc.contributor.department Industrial and Systems Engineering en_US
dc.date.accessioned 2009-01-22T15:56:30Z
dc.date.available 2009-01-22T15:56:30Z
dc.date.issued 2008-08-25 en_US
dc.description.abstract 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 data collected 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 Mozer and 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. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/26681
dc.publisher Georgia Institute of Technology en_US
dc.subject True positive rate en_US
dc.subject False positive rate en_US
dc.subject Classification en_US
dc.subject Disease diagnosis en_US
dc.subject Skill test en_US
dc.subject Robust estimation en_US
dc.subject Spatial models en_US
dc.subject Markov random field models en_US
dc.subject Spatial lattice data en_US
dc.subject Koziol-Green model and mean-shift model en_US
dc.subject Area under the curve en_US
dc.subject ROC curve en_US
dc.subject.lcsh Markov random fields
dc.subject.lcsh Lattice theory
dc.subject.lcsh Outliers (Statistics)
dc.title Robust estimation for spatial models and the skill test for disease diagnosis en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Lu, Jye-Chyi
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 4370d307-55cb-4bc3-9d5e-640823e16205
relation.isOrgUnitOfPublication 29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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