Bayesian Analysis of Temporal and Spatio-temporal Multivariate Environmental Data

High 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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Statistics

Department dc:contributor.department

Statistics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2019

Author dc:creator

• El Khouly, Mohamed Ibrahim

Chair dc:contributor.committeechair

• Ferreira, Marco A. R.

Committee members dc:contributor.committeemember

• Zick, Stephanie E.
• Franck, Christopher Thomas
• Smith, Eric P.

• Spatio-temporal analysis
• Bayesian analysis
• Multiscale models
• Dynamic linear models
• Climate change
• Tornado risk
• Markov Chain Monte Carlo
• Trend analysis
• Multivariate analysis
• Matrix-variate Gaussian distribution
• Reanalysis data.

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:19659

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/106456