Topological Data Analysis and theoretical statistical inference for time series dependent data and error in parametric choices

dc.contributor.advisorEnsor, Katherineen_US
dc.creatorAguilar, Alexen_US
dc.date.accessioned2022-09-23T21:28:41Zen_US
dc.date.available2023-08-01T05:01:14Zen_US
dc.date.created2022-08en_US
dc.date.issued2022-07-14en_US
dc.date.submittedAugust 2022en_US
dc.date.updated2022-09-23T21:28:41Zen_US
dc.description.abstractTopological data analysis extracts topological features by examining the shape of the data through persistent homology to produce topological summaries, such as the persistence landscape. While the persistence landscape makes it easier to conduct statistical analysis, the Strong Law of Large Numbers and a Central Limit Theorem for the persistence landscape applies to independent and identically distributed copies of a random variable. Therefore, we developed a Strong Law of Large Numbers and a Central Limit Theorem for the persistence landscape when the stochastic component of our series is driven by an autoregressive process of order one. Theoretical results for the persistence landscape are demonstrated computationally and applied to financial time series.en_US
dc.embargo.terms2023-08-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAguilar, Alex. "Topological Data Analysis and theoretical statistical inference for time series dependent data and error in parametric choices." (2022) Diss., Rice University. <a href="https://hdl.handle.net/1911/113333">https://hdl.handle.net/1911/113333</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/113333en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectTopological Data Analysisen_US
dc.subjectDependent Dataen_US
dc.subjectAutoregressive Processesen_US
dc.titleTopological Data Analysis and theoretical statistical inference for time series dependent data and error in parametric choicesen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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