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http://dx.doi.org/10.12652/Ksce.2018.38.6.0801

Development of Regression Models Resolving High-Dimensional Data and Multicollinearity Problem for Heavy Rain Damage Data  

Kim, Jeonghwan (Inha University)
Park, Jihyun (Inha University)
Choi, Changhyun (Inha University)
Kim, Hung Soo (Inha University)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.38, no.6, 2018 , pp. 801-808 More about this Journal
Abstract
The learning of the linear regression model is stable on the assumption that the sample size is sufficiently larger than the number of explanatory variables and there is no serious multicollinearity between explanatory variables. In this study, we investigated the difficulty of model learning when the assumption was violated by analyzing a real heavy rain damage data and we proposed to use a principal component regression model or a ridge regression model after integrating data to overcome the difficulty. We evaluated the predictive performance of the proposed models by using the test data independent from the training data, and confirmed that the proposed methods showed better predictive performances than the linear regression model.
Keywords
Heavy rain damage; Linear regression model; Principal component regression; Ridge regression;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
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