Master of Science (MS)
First Advisor's Name
First Advisor's Committee Title
Second Advisor's Name
B. M. Golam Kibria
Second Advisor's Committee Title
Third Advisor's Name
Third Advisor's Committee Title
predictive model, k-fold cross-validation, variable selection, prediction performance
Date of Defense
The impacts of disaster on the private sector are inevitable, but their risks can be managed and reduced by preventively evaluative measures. Disaster risk reduction index (DRRI) and Disaster Experience (DE) variables were investigated in a survey study in six Western Hemisphere cities within the private sector of various business sizes. Our thesis built and evaluated 16 predictive models of DRRI with 36 categorical predictors and N = 1162 observations. Four statistical methods for linear regression and five for classification as well as seven machine learning methods were utilized. We also used stepwise selection and regulation methods for variable selection. They improved the performance of some models. To evaluate and compare the prediction performance among all models, we used resampling 5-fold cross-validation (CV) to estimate the true mean squared error (MSE) and classification accuracy. The results indicated that the neural network was outperformed among all the predictive models with the highest classification accuracy.
Yin, Wupeng, "Statistical Modeling of Private Sector Participation in Disaster Risk Reduction Data" (2020). FIU Electronic Theses and Dissertations. 4567.
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