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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제32권 제1호
- 발행연도
- 2025.1
- 수록면
- 21 - 46 (26page)
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초록· 키워드
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The Lasso regression model is a penalty-based model that allows for variable selection from high-dimensional data. When Lasso regression is combined with a truncated power spline, this can be considered a knot selection problem from a nonparametric regression perspective. Using three algorithms—coordinate descent algorithm, quadratic programming, and alternating direction method of multipliers—, we compare the performance in fitting Lasso regression spline models. Additionally, we present a comparative experiment on the convergence of the coordinate descent algorithm according to the maximum number of iterations and the stopping threshold value in the glmnet function of R. Also, we compare the convergence rate of the algorithm under the various penalty parameter of the alternating direction method of multipliers with the coordinate descent algorithm. Through simulations and two real data analyses, we conduct numerical studies to verify the performance of the algorithms. By comparing their performance under various conditions, this study ultimately provides recommendations for the most suitable algorithm for different situations. The Lasso regression model is a penalization technique that facilitates variable selection from high-dimensional data. When combined with a truncated power spline, Lasso regression addresses the knot selection problem from a nonparametric regression perspective. We compare the performance of three methods—coordinate descent, quadratic programming, and the alternating direction method of multipliers—in fitting Lasso regression spline models. Through simulations and analyses of two real datasets, we conduct numerical studies to evaluate the performance of these methods. By comparing their performance under various conditions, this study ultimately offers recommendations for the most suitable algorithm for different scenarios.
목차
Abstract
1. Introduction
2. Lasso regression splines model
3. Method
4. Simulations
5. Real data analysis
6. Conclusion
References
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