Title:
Non-parametric statistical models using wavelets: Theory and methods

dc.contributor.advisor Vidakovic, Brani
dc.contributor.author Schnaidt Grez, German Augusto
dc.contributor.committeeMember Xie, Yao
dc.contributor.committeeMember Paynabar, Kamran
dc.contributor.committeeMember Goldsman, Dave
dc.contributor.committeeMember Romberg, Justin
dc.contributor.department Industrial and Systems Engineering
dc.date.accessioned 2020-05-20T16:56:51Z
dc.date.available 2020-05-20T16:56:51Z
dc.date.created 2019-05
dc.date.issued 2019-03-12
dc.date.submitted May 2019
dc.date.updated 2020-05-20T16:56:51Z
dc.description.abstract Machine Learning and Data Analytics have become key tools in the advancement of modern society, with a vast variety of applications exhibiting exponential growth in breadth and depth during the past few years. Moreover, the advancement of data-gathering technologies enables the availability of massive amounts of data, which fuels the opportunity for application and development of new analytics tools to obtain insights, making an effective and efficient use of it. This dissertation aims to contribute to the scientific existing methodologies in this context, with focus in the non-parametric statistical domain due to its robustness to prior modeling assumptions and flexibility of application in many different contexts. In light of this objective, four non-parametric methodologies based on wavelets are introduced and analyzed. Problems such as survival density estimation, non-linear additive regression and multiscale correlation analysis are covered, and each methodology is studied from both a theoretical and pragmatic perspectives. Moreover, a theoretical foundation for each proposed method is developed, and then its applicability and performance are illustrated using simulations studies, real data sets and comparison with previously published results in the field.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/62657
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Non parametric statistics
dc.subject Wavelets
dc.title Non-parametric statistical models using wavelets: Theory and methods
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Vidakovic, Brani
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 1463fd97-3d52-4269-afac-97f6f7f46fcd
relation.isOrgUnitOfPublication 29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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