| Title |
Development of Regression Models Resolving High-Dimensional Data and Multicollinearity Problem for Heavy Rain Damage Data |
| Authors |
김정환(Kim, Jeonghwan) ; 박지현(Park, Jihyun) ; 최창현(Choi, Changhyun) ; 김형수(Kim, Hung Soo) |
| DOI |
https://doi.org/10.12652/Ksce.2018.38.6.0801 |
| Keywords |
호우피해;선형회귀모형;주성분회귀;능형회귀 Heavy rain damage;Linear regression model;Principal component regression;Ridge regression |
| 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. |