Robust modeling

dc.contributor.advisorScott, David W.en_US
dc.creatorWojciechowski, William Conraden_US
dc.date.accessioned2009-06-04T06:40:31Zen_US
dc.date.available2009-06-04T06:40:31Zen_US
dc.date.issued2001en_US
dc.description.abstractIn this data-rich age, datasets often contain many observations and variables. Verifying the quality of a large dataset is a formidable task that is not to be completed by manual inspection. Therefore, methods that automatically perform well even when the dataset contains anomalous data points are needed. Robust procedures are designed to have this type of stability. A new general purpose robust estimator is introduced. This Bayesian procedure applies Gibbs sampling and data augmentation to achieve robustness by weighting the observations in the likelihood of Bayes' theorem. Because this new estimator relies upon simulation, it has several advantages over existing robust methods. The derivation of the new method will be presented along with examples that compare the new method to existing procedures.en_US
dc.format.extent104 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS STAT. 2001 WOJCIECHOWSKIen_US
dc.identifier.citationWojciechowski, William Conrad. "Robust modeling." (2001) Diss., Rice University. <a href="https://hdl.handle.net/1911/18043">https://hdl.handle.net/1911/18043</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/18043en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectStatisticsen_US
dc.titleRobust modelingen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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