On Some Aspects of Model Selection Variability

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dc.contributor.advisorWu, Yuehua
dc.creatorYang, Zhao Wei
dc.date.accessioned2016-11-25T14:14:04Z
dc.date.available2016-11-25T14:14:04Z
dc.date.copyright2016-08-19
dc.date.issued2016-11-25
dc.date.updated2016-11-25T14:14:04Z
dc.degree.disciplineMathematics & Statistics
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractIn this thesis, we investigate the data analytic approach to integrate the model selection uncertainty into the statistical inferences of high dimensional estimators. Two closed-form formulae of covariance matrices are derived for high dimensional bagging estimators, one for the nonparametric bootstrapping and the other for the parametric bootstrapping. Two simulation studies are completed in detail for demonstrating the validity of the new formulae. Several model selection methods --- the hypothesis testing, the Mallows' $C_p$, AIC, BIC and LASSO --- are compared in terms of the effects on the accuracy of bagging estimators in the context of multivariate linear regression. The confidence region and its coverage probability are also estimated for the bagging estimators with those model selection methods.
dc.identifier.urihttp://hdl.handle.net/10315/32758
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectStatistics
dc.subject.keywordsBootstrap Smoothing
dc.subject.keywordsModel Selection
dc.subject.keywordsInfluence Function
dc.subject.keywordsImportance Sampling
dc.subject.keywordsMultivariate Linear Regression
dc.subject.keywordsConfidence Region.
dc.titleOn Some Aspects of Model Selection Variability
dc.typeElectronic Thesis or Dissertation

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