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Hierarchical Statistical Models for Large Spatial Data in Uncertainty Quantification and Data Fusion

Shi, Hongxiang
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504802515691938

Abstract Details

2017, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
Modeling of spatial data often encounters computational bottleneck for large datasets and change- of-support effect for data at different resolutions. There is a rich literature on how to tackle these two problems but few gives a comprehensive solution for solving them together. This dissertation aims to develop hierarchical models that can alleviate those two problems together in uncertainty quantification and data fusion. For uncertainty quantification, a fully Bayesian hierarchical model combined with the nearest neighbor Gaussian process is proposed to produce consistent parameter inferences at different resolutions for a large spatial surface. Simulation studies demonstrate the ability of the proposed model to provide consistent parameter inferences at different resolutions with only a fraction of the computing time of the traditional method. This method is then applied to a real surface data. For data fusion, we propose a hierarchical model that can fuse two or more large spatial datasets with the exponential family of distributions. The ''change-of-support'' problem is handled along with the computational bottleneck by using a spatial random effect model for the underlying process. Through simulated and real data illustrations, the proposed data fusion method is demonstrated to possess predictive advantage over the univariate-process modeling approach by borrowing strength across processes.
Emily Kang, Ph.D. (Committee Chair)
Hang Joon Kim, Ph.D. (Committee Member)
Bledar Konomi, Ph.D. (Committee Member)
Siva Sivaganesan, Ph.D. (Committee Member)
Xia Wang, Ph.D. (Committee Member)
109 p.
Statistics
Spatial Hierarchical Modeling; Nearest Neighbor Gaussian Process; Spatial Random Effect Model; EM Algorithm; MCMC Algrotihm

Recommended Citations

Citations

  • Shi, H. (2017). Hierarchical Statistical Models for Large Spatial Data in Uncertainty Quantification and Data Fusion [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504802515691938

    APA Style (7th edition)

  • Shi, Hongxiang. Hierarchical Statistical Models for Large Spatial Data in Uncertainty Quantification and Data Fusion. 2017. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504802515691938.

    MLA Style (8th edition)

  • Shi, Hongxiang. "Hierarchical Statistical Models for Large Spatial Data in Uncertainty Quantification and Data Fusion." Doctoral dissertation, University of Cincinnati, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504802515691938

    Chicago Manual of Style (17th edition)

ucin1504802515691938
208
© 2017, some rights reserved.Creative Commons License Hierarchical Statistical Models for Large Spatial Data in Uncertainty Quantification and Data Fusion by Hongxiang Shi is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.