Show simple item record

dc.contributor.advisorLiu, Jun S.
dc.contributor.authorNing, Shaoyang
dc.date.accessioned2019-05-16T12:40:57Z
dc.date.created2018-11
dc.date.issued2018-09-06
dc.date.submitted2018
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:39947181*
dc.description.abstractThis dissertation consists of four self-contained chapters where statistical methods for integrative data analysis with applications in biology, epidemiology and finance are introduced and discussed. Chapters 1 and 2 focus on aggregating multi-source, high-throughput biological data for enhancement in functionality annotation and prediction: in chapter 1, we introduce an integrative algorithm based on Bayesian hidden Markov tree models to incorporate genes' phylogenetic profile and their inferred evolutionary histories for gene clustering and functional prediction; in chapter 2, we work on aggregating the genetic and pharmacological profiling data in Cancer Cell Line Encyclopedia to provide predictions on the mechanisms and targets of cancer-treating drugs. In chapter 3, we move to integrating large-scale digital data and spatio-temporal epidemics data, and show how to improve robustness and accuracy in localized influenza tracking by effectively combining Internet search data and traditional disease surveillance data. In chapter 4, we take a more general view as to link multi-dimension data with a non-parametric Bayesian copula model and predict the irregular covariance structure between stock price and index data during the financial crisis.
dc.description.sponsorshipStatistics
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectStatistics
dc.subjectBiology, Biostatistics
dc.subjectEconomics, Finance
dc.titleNew Statistical Methods for Integrative Data Analysis and Applications in Biology, Epidemiology and Finance
dc.typeThesis or Dissertation
dash.depositing.authorNing, Shaoyang
dc.date.available2019-05-16T12:40:57Z
thesis.degree.date2018
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
dc.contributor.committeeMemberKou, S.C. Samuel
dc.contributor.committeeMemberShephard, Neil
dc.type.materialtext
thesis.degree.departmentStatistics
dash.identifier.vireohttp://etds.lib.harvard.edu/gsas/admin/view/2445
dc.description.keywordsIntegrative analysis; Bayesian Markov Tree Model; Copula; influenza tracking; cancer cell line;
dash.author.emailshaoyangning@gmail.com


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record