dc.contributor.author Gelsinger, Megan Lynne dc.date.accessioned 2021-12-20T20:48:12Z dc.date.available 2022-03-10T07:00:20Z dc.date.issued 2021-08 dc.identifier.other Gelsinger_cornellgrad_0058F_12624 dc.identifier.other http://dissertations.umi.com/cornellgrad:12624 dc.identifier.uri https://hdl.handle.net/1813/110548 dc.description 126 pages dc.description.abstract Modern technology allows researchers across disciplines to capture vast quantities of data -- big" data -- to help answer some of their most pressing scientific questions. The analysis of big data, though, is complicated by its computational feasibility and the availability of appropriate statistical techniques. These problems only grow in complexity when working with dependent data, such as data which is recorded across time (temporal dependence), or data which is recorded across space (spatial dependence). In this work, we offer tools to aid analysis of big data in both domains. In the temporal domain, we study a classification approach for high-dimensional times series data. The motivating dataset for this work comes from biological time series data simultaneously measured across many electrical frequencies. Our results suggest the plausibility of accurately classifying a variety of cell lines, thus providing researchers with a means of checking" the cell types under study prior to reporting any results. In the spatial domain, we present work for fitting statistical models to large spatial point pattern data. In particular, we combine spectral and Laplace approximations, among others, in an EM algorithm, denoted SLEM, to approximately fit the widely popular log-Gaussian Cox process to these big spatial datasets. Given the utility of SLEM, we are able to conduct a large-scale lightning dynamics study across the contiguous United States, where we make interesting observations about the relationship between lightning occurrence and several environmental covariates. dc.language.iso en dc.title Spatial and Temporal Approaches to Analyzing Big Data dc.type dissertation or thesis thesis.degree.discipline Statistics thesis.degree.grantor Cornell University thesis.degree.level Doctor of Philosophy thesis.degree.name Ph. D., Statistics dc.contributor.chair Matteson, David dc.contributor.committeeMember Guinness, Joe dc.contributor.committeeMember Booth, James dc.contributor.committeeMember Basu, Sumanta dcterms.license https://hdl.handle.net/1813/59810 dc.identifier.doi https://doi.org/10.7298/f24d-3d69
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