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dc.contributor.authorHuang, Cheng
dc.date.accessioned2018-08-20T15:27:51Z
dc.date.available2018-08-20T15:27:51Z
dc.date.created2017-08
dc.date.issued2017-05-15
dc.date.submittedAugust 2017
dc.identifier.urihttp://hdl.handle.net/1853/60120
dc.description.abstractParameter estimation and hypotheses testing are two fundamental problems in statistics. Many existing methods have been developed for the problems with moderate amount of data. Unfortunately, some of those methods could be computationally costly or even infeasible when the volume of data is high. This dissertation is an attempt to fulfill the needs for computationally efficient methods in statistics. The first part of this dissertation describes a one-step approach to enhance an existing simple averaging estimator for distributed statistical inference, which achieve the same convergence rate with the estimator using centralized data. In the second part, we develop an efficient algorithm with reduced computational complexity for distance covariance and apply this new algorithm to derive a test of independence, which enjoys nearly the same asymptotic efficiency with the state-of-the-art distance covariance. The third part is a statistically and computationally efficient two-sample test based on energy statistics and random projections.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectDistributed statistical inference
dc.subjectTest of Independence
dc.subjectTwo-Sample test
dc.subjectOne-step
dc.subjectRandom projection
dc.titleSome computationally efficient methods in statistics and their applications in parameter estimation and hypotheses testing
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentIndustrial and Systems Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberHuo, Xiaoming
dc.contributor.committeeMemberWu, Jeff
dc.contributor.committeeMemberMei, Yajun
dc.contributor.committeeMemberXie, Yao
dc.contributor.committeeMemberKolthinskii, Vladimir
dc.date.updated2018-08-20T15:27:51Z


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