Variable Selection via the Sparse Net

• 발행기관: 한국자료분석학회
• 저자명: Sunghoon Kwon Jinyeon Jo Hosik Choi Sangin Lee
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.21 No.3, 1111~1120쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2019.06.30

영문 초록

Variable selection is an important problem when the model includes many noisy variables. For years, the sparse penalized approaches have been proposed for the problem. Examples are the least absolute selection and shrinkage operator (LASSO) and smoothly clipped absolute deviation penalty. The sparse penalized approaches are known to have more stable sampling properties compared with the traditional best subset selection approaches, keeping higher computational efficiency. In this paper, we propose a new penalty, sparse net (SNET), that can be used for variable selection and parameter estimation in the linear regression model. The SNET consists of two penalties: the LASSO and the negative ridge. The SNET shares the same spirit with the elastic net but the ridge effect is negatively imposed on the regression coefficients to avoid unnecessary selection and biases. In general, the SNET penalized sum of squared residuals is non-convex, depending on the size of negative ridge effect, which requires non-convex optimization algorithm. For this issue, we introduce an efficient algorithm for implementing the SNET penalized estimator. Several numerical examples show that the SNET can be a nice competitor with existing penalties.

목차

1. Introduction

2. Sparse Net

3. Numerical Studies

4. Concluding Remarks

키워드

elastic net LASSO SCAD sparse net variable selection