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Hierarchical Nearest Neighbor Co-kriging Gaussian Process For Large And Multi-Fidelity Spatial Dataset

Cheng, Si
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613750570927821

Abstract Details

2021, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
Spatial datasets with varying fidelity are often obtained by different platform in remote sensing. A single composite feature that includes adequate information from multiple data sources is preferred for the statistical inference. Modeling multi-fidelity dataset usually encounters the challenges of computational complexity and complicated correlation structure due to large amount of observations for spatial areas that may or may not overlap or have same spatial footprints. Few of previous researches provide the comprehensive solution for these challenges. This dissertation develops a hierarchical nearest neighbor co-kriging Gaussian process(NNCGP) for the analysis of large irregularly spaced and multi-fidelity spatial dataset. The dissertation also proposed efficient algorithms of NNCGP method for improved performance and further computational efficiency. Simulation studies demonstrate the proposed NNCGP models are capable of providing more reliable statistical inference, improved prediction performance and reduced amount of running time comparing to the classical models. The methods are also applied to high-resolution infrared radiation sounder (HIRS) data-sets gathered daily from two polar orbiting satellite series (POES) of the National Oceanic and Atmospheric Administration (NOAA).
Bledar Konomi, Ph.D. (Committee Chair)
Won Chang, Ph.D. (Committee Member)
Emily Kang, Ph.D. (Committee Member)
Siva Sivaganesan, Ph.D. (Committee Member)
117 p.
Statistics
Bayesian; Gaussian Process; Hierarchal model; Co-kriging; Big data; Spatial Data

Recommended Citations

Citations

  • Cheng, S. (2021). Hierarchical Nearest Neighbor Co-kriging Gaussian Process For Large And Multi-Fidelity Spatial Dataset [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613750570927821

    APA Style (7th edition)

  • Cheng, Si. Hierarchical Nearest Neighbor Co-kriging Gaussian Process For Large And Multi-Fidelity Spatial Dataset. 2021. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613750570927821.

    MLA Style (8th edition)

  • Cheng, Si. "Hierarchical Nearest Neighbor Co-kriging Gaussian Process For Large And Multi-Fidelity Spatial Dataset." Doctoral dissertation, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613750570927821

    Chicago Manual of Style (17th edition)

ucin1613750570927821
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This open access ETD is published by University of Cincinnati and OhioLINK.