Show simple item record

dc.contributor.advisorTang, Cheng-Yong
dc.contributor.advisorKrafty, Robert T.
dc.creatorBruce, Scott Alan
dc.date.accessioned2020-11-03T16:23:28Z
dc.date.available2020-11-03T16:23:28Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/20.500.12613/2640
dc.description.abstractThis thesis proposes novel methods to address specific challenges in analyzing the frequency- and time-domain properties of nonstationary time series data motivated by the study of electrophysiological signals. A new method is proposed for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates. The procedure adaptively partitions the grid of time and covariate values into an unknown number of approximately stationary blocks and nonparametrically estimates local spectra within blocks through penalized splines. The approach is formulated in a fully Bayesian framework, in which the number and locations of partition points are random, and fit using reversible jump Markov chain Monte Carlo techniques. Estimation and inference averaged over the distribution of partitions allows for the accurate analysis of spectra with both smooth and abrupt changes. The new methodology is used to analyze the association between the time-varying spectrum of heart rate variability and self-reported sleep quality in a study of older adults serving as the primary caregiver for their ill spouse. Another method proposed in this dissertation develops a unique framework for automatically identifying bands of frequencies exhibiting similar nonstationary behavior. This proposal provides a standardized, unifying approach to constructing customized frequency bands for different signals under study across different settings. A frequency-domain, iterative cumulative sum procedure is formulated to identify frequency bands that exhibit similar nonstationary patterns in the power spectrum through time. A formal hypothesis testing procedure is also developed to test which, if any, frequency bands remain stationary. This method is shown to consistently estimate the number of frequency bands and the location of the upper and lower bounds defining each frequency band. This method is used to estimate frequency bands useful in summarizing nonstationary behavior of full night heart rate variability data.
dc.format.extent109 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectStatistics
dc.subjectBayesian Analysis
dc.subjectFrequency Band Estimation
dc.subjectNonparametric Statistics
dc.subjectNonstationary Time Series Analysis
dc.titleSTATISTICAL METHODS FOR SPECTRAL ANALYSIS OF NONSTATIONARY TIME SERIES
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberDong, Yuexiao
dc.contributor.committeememberZhao, Zhigen
dc.contributor.committeememberShou, Haochang
dc.description.departmentStatistics
dc.relation.doihttp://dx.doi.org/10.34944/dspace/2622
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
refterms.dateFOA2020-11-03T16:23:28Z


Files in this item

Thumbnail
Name:
TETDEDXBruce-temple-0225E-13326.pdf
Size:
8.159Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record