Double Penalized Expectile Regression for Linear Mixed Effects Model
This paper constructs the double penalized expectile regression for linear mixed effects model, which can estimate coefficient and choose variable for random and fixed effects simultaneously. The method based on the linear mixed effects model by cojoining double penalized expectile regression. For t...
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MDPI AG
2022-07-01
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| Online Access: | https://www.mdpi.com/2073-8994/14/8/1538 |
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| author | Sihan Gao Jiaqing Chen Zihao Yuan Jie Liu Yangxin Huang |
| author_facet | Sihan Gao Jiaqing Chen Zihao Yuan Jie Liu Yangxin Huang |
| author_sort | Sihan Gao |
| collection | DOAJ |
| description | This paper constructs the double penalized expectile regression for linear mixed effects model, which can estimate coefficient and choose variable for random and fixed effects simultaneously. The method based on the linear mixed effects model by cojoining double penalized expectile regression. For this model, this paper proposes the iterative Lasso expectile regression algorithm to solve the parameter for this mode, and the Schwarz Information Criterion (SIC) and Generalized Approximate Cross-Validation Criterion (GACV) are used to choose the penalty parameters. Additionally, it establishes the asymptotic normality of the expectile regression coefficient estimators that are suggested. Though simulation studies, we examine the effects of coefficient estimation and the variable selection at varying expectile levels under various conditions, including different signal-to-noise ratios, random effects, and the sparsity of the model. In this work, founding that the proposed method is robust to various error distributions at every expectile levels, and is superior to the double penalized quantile regression method in the robustness of excluding inactive variables. The suggested method may still accurately exclude inactive variables and select important variables with a high probability for high-dimensional data. The usefulness of doubly penalized expectile regression in applications is illustrated through a case study using CD4 cell real data. |
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| format | Article |
| id | doaj.art-e8dde340eeff454f9309b519040f2e28 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T03:47:55Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-e8dde340eeff454f9309b519040f2e282023-12-03T14:32:41ZengMDPI AGSymmetry2073-89942022-07-01148153810.3390/sym14081538Double Penalized Expectile Regression for Linear Mixed Effects ModelSihan Gao0Jiaqing Chen1Zihao Yuan2Jie Liu3Yangxin Huang4Department of Statistics, College of Science, Wuhan University of Technology, Wuhan 430070, ChinaDepartment of Statistics, College of Science, Wuhan University of Technology, Wuhan 430070, ChinaDepartment of Statistics, College of Science, Wuhan University of Technology, Wuhan 430070, ChinaDepartment of Statistics, College of Science, Wuhan University of Technology, Wuhan 430070, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612, USAThis paper constructs the double penalized expectile regression for linear mixed effects model, which can estimate coefficient and choose variable for random and fixed effects simultaneously. The method based on the linear mixed effects model by cojoining double penalized expectile regression. For this model, this paper proposes the iterative Lasso expectile regression algorithm to solve the parameter for this mode, and the Schwarz Information Criterion (SIC) and Generalized Approximate Cross-Validation Criterion (GACV) are used to choose the penalty parameters. Additionally, it establishes the asymptotic normality of the expectile regression coefficient estimators that are suggested. Though simulation studies, we examine the effects of coefficient estimation and the variable selection at varying expectile levels under various conditions, including different signal-to-noise ratios, random effects, and the sparsity of the model. In this work, founding that the proposed method is robust to various error distributions at every expectile levels, and is superior to the double penalized quantile regression method in the robustness of excluding inactive variables. The suggested method may still accurately exclude inactive variables and select important variables with a high probability for high-dimensional data. The usefulness of doubly penalized expectile regression in applications is illustrated through a case study using CD4 cell real data.https://www.mdpi.com/2073-8994/14/8/1538double penalizedmixed effectsexpectile regressionLasso algorithm |
| spellingShingle | Sihan Gao Jiaqing Chen Zihao Yuan Jie Liu Yangxin Huang Double Penalized Expectile Regression for Linear Mixed Effects Model Symmetry double penalized mixed effects expectile regression Lasso algorithm |
| title | Double Penalized Expectile Regression for Linear Mixed Effects Model |
| title_full | Double Penalized Expectile Regression for Linear Mixed Effects Model |
| title_fullStr | Double Penalized Expectile Regression for Linear Mixed Effects Model |
| title_full_unstemmed | Double Penalized Expectile Regression for Linear Mixed Effects Model |
| title_short | Double Penalized Expectile Regression for Linear Mixed Effects Model |
| title_sort | double penalized expectile regression for linear mixed effects model |
| topic | double penalized mixed effects expectile regression Lasso algorithm |
| url | https://www.mdpi.com/2073-8994/14/8/1538 |
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