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A Unified Exposure Prediction Approach for Multivariate Spatial Data: From Predictions to Health Analysis

Zhu, Zheng
http://rave.ohiolink.edu/etdc/view?acc_num=ucin155437434818942

Abstract Details

2019, PhD, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
Epidemiological cohort studies of health effect often rely on spatial models to predict ambient air pollutant concentrations at participants' residential addresses. Compared with traditional linear regression models, spatial models such as Kriging provide us accurate prediction by taking into account spatial correlations within data. Spatial model utilizes regression covariates from high dimensional database provided by geographical information system (GIS). This modeling requires dimension reduction techniques such as partial least squares, lasso, elastic net, etc. In the first chapter of this thesis, we presented a comparison of performance of four potential spatial prediction models. The first two approaches are based on universal kriging (UK). The third and fourth approaches are based on random forest and Bayesian additive regression trees (BART), with some degree of spatial smoothing. Multivariate spatial models are often considered for point-referenced spatial data, which contains multiple measurements at each monitoring location and therefore correlation between measurements is anticipated. In the second chapter of the thesis, we proposed a chain model, for analyzing multivariate spatial data. We showed that chain model outperform other spaital models such as universal kriging and coregionalization model. In the third chapter, we connect our spatial analysis with epidemiological studies of health effects of environmental chemical mixtures. Specifically, we investigated the relationship between environmental chemical mixture exposure and cognitive and motor development of infants. We proposed a framework to analyze health effects of environmental chemical mixtures. We first perform dimension reduction of the exposure variables using principal component analysis. In the second stage, we applied a best subset regression to obtain the final model.
Roman Jandarov, Ph.D. (Committee Chair)
Sivaraman Balachandran, Ph.D. (Committee Member)
Won Chang, Ph.D. (Committee Member)
Marepalli Rao, Ph.D. (Committee Member)
77 p.
Statistics
Spatial model; air pollution; high dimensional data; multivariate spatial model; machine learning; health effect model

Recommended Citations

Citations

  • Zhu, Z. (2019). A Unified Exposure Prediction Approach for Multivariate Spatial Data: From Predictions to Health Analysis [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin155437434818942

    APA Style (7th edition)

  • Zhu, Zheng. A Unified Exposure Prediction Approach for Multivariate Spatial Data: From Predictions to Health Analysis. 2019. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin155437434818942.

    MLA Style (8th edition)

  • Zhu, Zheng. "A Unified Exposure Prediction Approach for Multivariate Spatial Data: From Predictions to Health Analysis." Doctoral dissertation, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin155437434818942

    Chicago Manual of Style (17th edition)

ucin155437434818942
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This open access ETD is published by University of Cincinnati and OhioLINK.