LASSO-based predictor section in downscaling GCM data

• 영문명: LASSO-based predictor section in downscaling GCM data
• 발행기관: 한국방재학회
• 저자명: Lee, Taesam Hammami, Dorra Lee, TTaesam Ouarda, Taha B.M.J.
• 간행물 정보: 『3. 한국방재학회 학술대회논문집』2014년, 384~384쪽, 전체 1쪽
• 주제분류: 공학 > 기타공학
• 파일형태: PDF
• 발행일자: 2014.02.19

영문 초록

The objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Quebec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters for comparison.

키워드

Downscaling LASSO Predictor selection Stepwise Regression