Bayesian Analysis of Temporal and Spatio-temporal Multivariate Environmental Data

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dc.contributor.authorEl Khouly, Mohamed Ibrahimen
dc.contributor.committeechairFerreira, Marco A. R.en
dc.contributor.committeememberZick, Stephanie E.en
dc.contributor.committeememberFranck, Christopher Thomasen
dc.contributor.committeememberSmith, Eric P.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2021-10-31T06:00:06Zen
dc.date.available2021-10-31T06:00:06Zen
dc.date.issued2019-05-09en
dc.description.abstractHigh dimensional space-time datasets are available nowadays in various aspects of life such as economy, agriculture, health, environment, etc. Meanwhile, it is challenging to reveal possible connections between climate change and weather extreme events such as hurricanes or tornadoes. In particular, the relationship between tornado occurrence and climate change has remained elusive. Moreover, modeling multivariate spatio-temporal data is computationally expensive. There is great need to computationally feasible models that account for temporal, spatial, and inter-variables dependence. Our research focuses on those areas in two ways. First, we investigate connections between changes in tornado risk and the increase in atmospheric instability over Oklahoma. Second, we propose two multiscale spatio-temporal models, one for multivariate Gaussian data, and the other for matrix-variate Gaussian data. Those frameworks are novel additions to the existing literature on Bayesian multiscale models. In addition, we have proposed parallelizable MCMC algorithms to sample from the posterior distributions of the model parameters with enhanced computations.en
dc.description.abstractgeneralOver 1000 tornadoes are reported every year in the United States causing massive losses in lives and possessions according to the National Oceanic and Atmospheric Administration. Therefore, it is worthy to investigate possible connections between climate change and tornado occurrence. However, there are massive environmental datasets in three or four dimensions (2 or 3 dimensional space, and time), and the relationship between tornado occurrence and climate change has remained elusive. Moreover, it is computationally expensive to analyze those high dimensional space-time datasets. In part of our research, we have found a significant relationship between occurrence of strong tornadoes over Oklahoma and meteorological variables. Some of those meteorological variables have been affected by ozone depletion and emissions of greenhouse gases. Additionally, we propose two Bayesian frameworks to analyze multivariate space-time datasets with fast and feasible computations. Finally, our analyses indicate different patterns of temperatures at atmospheric altitudes with distinctive rates over the United States.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:19659en
dc.identifier.urihttp://hdl.handle.net/10919/106456en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSpatio-temporal analysisen
dc.subjectBayesian analysisen
dc.subjectMultiscale modelsen
dc.subjectDynamic linear modelsen
dc.subjectClimate changeen
dc.subjectTornado risken
dc.subjectMarkov Chain Monte Carloen
dc.subjectTrend analysisen
dc.subjectMultivariate analysisen
dc.subjectMatrix-variate Gaussian distributionen
dc.subjectReanalysis data.en
dc.titleBayesian Analysis of Temporal and Spatio-temporal Multivariate Environmental Dataen
dc.typeDissertationen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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