지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제27권 제4호
- 발행연도
- 2020.7
- 수록면
- 445 - 458 (14page)
이용수
초록· 키워드
오류제보하기
The least absolute shrinkage and selection operator (LASSO) is a popular method for a high-dimensional regression model. LASSO has high prediction accuracy; however, it also selects many irrelevant variables. In this paper, we consider the moderately clipped LASSO (MCL) for the high-dimensional generalized linear model which is a hybrid method of the LASSO and minimax concave penalty (MCP). The MCL preserves advantages of the LASSO and MCP since it shows high prediction accuracy and successfully selects relevant variables. We prove that the MCL achieves the oracle property under some regularity conditions, even when the number of parameters is larger than the sample size. An efficient algorithm is also provided. Various numerical studies confirm that the MCL can be a better alternative to other competitors.
목차
Abstract
1. Introduction
2. Moderately clipped LASSO for the GLM
3. Asymptotic properties
4. Numerical studies
5. Concluding remarks
References
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Moderately Clipped LASSO for the Sparse High-Dimensional Logistic Regression Models
Journal of The Korean Data Analysis Society
2015 .01
-
High-dimensional linear discriminant analysis with moderately clipped LASSO
CSAM(Communications for Statistical Applications and Methods)
2021 .01
-
Local Influence in LASSO Regression
Journal of The Korean Data Analysis Society
2016 .12
-
Adaptive lasso를 이용한 희박벡터자기회귀모형에서의 변수 선택
응용통계연구
2016 .02
-
Effect of outliers on the variable selection by the regularized regression
CSAM(Communications for Statistical Applications and Methods)
2018 .03
-
An Additive Sparse Penalty for Variable Selection in High-Dimensional Linear Regression Model
CSAM(Communications for Statistical Applications and Methods)
2015 .03
-
Fused lasso 회귀 모형 기반의 대학병원 수익성에 대한 요인 연구
한국데이터정보과학회지
2018 .01
-
Robust penalized estimation via Welsch loss with group Lasso
한국데이터정보과학회지
2021 .05
-
비정상 자기회귀모형에서의 벌점화 추정 기법에 대한 연구
응용통계연구
2019 .12
-
Variable selection and prediction performance of penalized two-part regression with community-based crime data application
CSAM(Communications for Statistical Applications and Methods)
2024 .07
-
Penalized variable selection for accelerated failure time models
CSAM(Communications for Statistical Applications and Methods)
2018 .11
-
A convenient approach for penalty parameter selection in robust lasso regression
CSAM(Communications for Statistical Applications and Methods)
2017 .11
-
Mixed degree regression function estimation using weighted lasso penalty
한국데이터정보과학회지
2022 .05
-
A Study on Bias Effect on Model Selection Criteria in Graphical Lasso
Quantitative Bio-Science
2018 .11
-
Penalized maximum likelihood estimation with symmetric log-concave errors and LASSO penalty
CSAM(Communications for Statistical Applications and Methods)
2022 .11
-
모형 선택 기준들에 대한 LASSO 회귀 모형 편의의 영향 연구
응용통계연구
2016 .06
-
Comparison of algorithms for estimating regression splines using lasso penalty
CSAM(Communications for Statistical Applications and Methods)
2025 .01
-
인공지능을 적용한 시력데이터 분석 : 기계학습 모델간 비교 및 최적모델제시
대한시과학회지
2020 .01
-
LASSO 방법을 이용한 프라이버시 침해 우려 행태 분석
Journal of The Korean Data Analysis Society
2019 .01
-
Penalized rank regression estimator with the smoothly clipped absolute deviation function
CSAM(Communications for Statistical Applications and Methods)
2017 .11
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2023-310-001442094