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Statistical modeling and computing for climate data

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dc.contributor.authorHewitt, Joshua, author
dc.contributor.authorHoeting, Jennifer A., advisor
dc.contributor.authorCooley, Daniel S., committee member
dc.contributor.authorWang, Haonan, committee member
dc.contributor.authorKampf, Stephanie K., committee member
dc.date.accessioned2019-06-14T17:06:54Z
dc.date.available2019-06-14T17:06:54Z
dc.date.issued2019
dc.description.abstractThe motivation for this thesis is to provide improved statistical models and approaches to statistical computing for analyzing climate patterns over short and long distances. In particular, information needs for water managers motivate my research. Statistical models and computing techniques exist in a careful balance because climate data are generated by physical processes that can yield computationally intractable statistical models. Simplified or approximate statistical models are often required for practical data analyses. Critically, model complexity is moderated as much by research needs and available data as it is by computational capabilities. I start by developing a weighted likelihood that improves estimates of high quantiles for extreme precipitation (i.e., return levels) from latent spatial extremes models. In my second project, I develop a geostatistical model that accounts for the influence of remotely observed spatial covariates. The model improves prediction of regional precipitation and related climate variables that are influenced by global-scale processes known as teleconnections. I make the model more accessible by providing an R package that includes visualization, estimation, prediction, and diagnostic tools. The models from my first two projects require estimating large numbers of latent effects, so their implementations rely on computationally efficient methods. My third project proposes a deterministic, quadrature-based computational approach for estimating hierarchical Bayesian models with many hyperparameters, including those from my first two projects. The deterministic method is easily parallelizable and can require substantially less computational effort than common stochastic alternatives, like Monte Carlo methods. Notably, my quadrature-based method can also improve the computational efficiency of other recent, fast, deterministic approaches for estimating hierarchical Bayesian models, such as the integrated nested Laplace approximation (INLA). I also make the quadrature-based method accessible through an R package that provides inference for user-specified hierarchical models. Throughout my thesis, I demonstrate how improved models, more efficient computational methods, and accessible software allow modeling of large, complex climate data.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierHewitt_colostate_0053A_15456.pdf
dc.identifier.urihttps://hdl.handle.net/10217/195408
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectclimate
dc.subjectstatistical computing
dc.subjecthierarchical modeling
dc.subjectBayesian statistics
dc.titleStatistical modeling and computing for climate data
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineStatistics
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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