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You searched for +publisher:"University of Saskatchewan" +contributor:("Feng, Cindy.Xin"). One record found.

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1. Qiu, Shi. Cross-validatory Model Comparison and Divergent Regions Detection using iIS and iWAIC for Disease Mapping.

Degree: 2015, University of Saskatchewan

The well-documented problems associated with mapping raw rates of disease have resulted in an increased use of Bayesian hierarchical models to produce maps of "smoothed'' estimates of disease rates. Two statistical problems arise in using Bayesian hierarchical models for disease mapping. The first problem is in comparing goodness of fit of various models, which can be used to test different hypotheses. The second problem is in identifying outliers/divergent regions with unusually high or low residual risk of disease, or those whose disease rates are not well fitted. The results of outlier detection may generate further hypotheses as to what additional covariates might be necessary for explaining the disease. Leave-one-out cross-validatory (LOOCV) model assessment has been used for these two problems. However, actual LOOCV is time-consuming. This thesis introduces two methods, namely iIS and iWAIC, for approximating LOOCV, using only Markov chain samples simulated from a posterior distribution based on a full data set. In iIS and iWAIC, we first integrate the latent variables without reference to holdout observation, then apply IS and WAIC approximations to the integrated predictive density and evaluation function. We apply iIS and iWAIC to two real data sets. Our empirical results show that iIS and iWAIC can provide significantly better estimation of LOOCV model assessment than existing methods including DIC, Importance Sampling, WAIC, posterior checking and Ghosting methods. Advisors/Committee Members: Li, Longhai, Feng, Cindy.Xin, Bickis, Mikelis G..

Subjects/Keywords: cross-validation; disease mapping; importance sampling; WAIC

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APA (6th Edition):

Qiu, S. (2015). Cross-validatory Model Comparison and Divergent Regions Detection using iIS and iWAIC for Disease Mapping. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/ETD-2015-03-1988

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Qiu, Shi. “Cross-validatory Model Comparison and Divergent Regions Detection using iIS and iWAIC for Disease Mapping.” 2015. Thesis, University of Saskatchewan. Accessed February 18, 2019. http://hdl.handle.net/10388/ETD-2015-03-1988.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Qiu, Shi. “Cross-validatory Model Comparison and Divergent Regions Detection using iIS and iWAIC for Disease Mapping.” 2015. Web. 18 Feb 2019.

Vancouver:

Qiu S. Cross-validatory Model Comparison and Divergent Regions Detection using iIS and iWAIC for Disease Mapping. [Internet] [Thesis]. University of Saskatchewan; 2015. [cited 2019 Feb 18]. Available from: http://hdl.handle.net/10388/ETD-2015-03-1988.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Qiu S. Cross-validatory Model Comparison and Divergent Regions Detection using iIS and iWAIC for Disease Mapping. [Thesis]. University of Saskatchewan; 2015. Available from: http://hdl.handle.net/10388/ETD-2015-03-1988

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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