Multivariate Independence and k-sample Testing

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dc.contributor.advisorVogelstein, Joshua T
dc.contributor.committeeMemberPriebe, Carey E
dc.contributor.committeeMemberMehta, Ronak
dc.creatorPanda, Sambit
dc.creator.orcid0000-0001-8455-4243
dc.date.accessioned2020-06-21T20:37:42Z
dc.date.available2020-06-21T20:37:42Z
dc.date.created2020-05
dc.date.issued2020-05-05
dc.date.submittedMay 2020
dc.date.updated2020-06-21T20:37:43Z
dc.description.abstractWith the increase in the amount of data in many fields, a method to consistently and efficiently decipher relationships within high dimensional data sets is important. Because many modern datasets are multivariate, univariate tests are not applicable. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. We introduce hyppo, which includes many state of the art multivariate testing procedures. This thesis provides details for the implementations of each of the tests within a test hyppo as well as extensive power and run-time benchmarks on a suite of high-dimensional simulations previously used in different publications. The documentation and all releases for hyppo are available at https://hyppo.neurodata.io.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/62706
dc.language.isoen_US
dc.publisherJohns Hopkins University
dc.publisher.countryUSA
dc.subjectPython, independence, k-sample, multivariate
dc.titleMultivariate Independence and k-sample Testing
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentBiomedical Engineering
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorJohns Hopkins University
thesis.degree.grantorWhiting School of Engineering
thesis.degree.levelMasters
thesis.degree.nameM.S.E.
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