Variable selection and collinearity processing for multivariate data via row-elastic-net regularization

2021 ◽  
Author(s):  
Bingzhen Chen ◽  
Wenjuan Zhai ◽  
Lingchen Kong
Keyword(s):  
Variable Selection ◽  
Multivariate Data ◽  
Elastic Net ◽  
Elastic Net Regularization

Variable selection and regression analysis for the prediction of mortality rates associated with foodborne diseases

2016 ◽  
Vol 144 (9) ◽  
pp. 1959-1973 ◽  
Author(s):  
E. AMENE ◽  
L. A. HANSON ◽  
E. A. ZAHN ◽  
S. R. WILD ◽  
D. DÖPFER
Keyword(s):  
Correlation Analysis ◽  
Variable Selection ◽  
Mortality Rates ◽  
Elastic Net ◽  
Hierarchical Regression ◽  
Foodborne Diseases ◽  
Bayesian Hierarchical ◽  
Elastic Net Regularization ◽  
Prediction Of Mortality ◽  
Data Gaps

SUMMARYThe purpose of this study was to apply a novel statistical method for variable selection and a model-based approach for filling data gaps in mortality rates associated with foodborne diseases using the WHO Vital Registration mortality dataset. Correlation analysis and elastic net regularization methods were applied to drop redundant variables and to select the most meaningful subset of predictors. Whenever predictor data were missing, multiple imputation was used to fill in plausible values. Cluster analysis was applied to identify similar groups of countries based on the values of the predictors. Finally, a Bayesian hierarchical regression model was fit to the final dataset for predicting mortality rates. From 113 potential predictors, 32 were retained after correlation analysis. Out of these 32 predictors, eight with non-zero coefficients were selected using the elastic net regularization method. Based on the values of these variables, four clusters of countries were identified. The uncertainty of predictions was large for countries within clusters lacking mortality rates, and it was low for a cluster that had mortality rate information. Our results demonstrated that, using Bayesian hierarchical regression models, a data-driven clustering of countries and a meaningful subset of predictors can be used to fill data gaps in foodborne disease mortality.

scholarly journals Research of magnetic particle imaging reconstruction based on the elastic net regularization

2021 ◽  
Vol 69 ◽  
pp. 102823
Author(s):  
Xiaojun Chen ◽  
Zhenqi Jiang ◽  
Xiao Han ◽  
Xiaolin Wang ◽  
Xiaoying Tang
Keyword(s):  
Magnetic Particle ◽  
Elastic Net ◽  
Elastic Net Regularization ◽  
Magnetic Particle Imaging ◽  
Imaging Reconstruction ◽  
Particle Imaging

scholarly journals Joint MT and Gravity Inversion Using Structural Constraints: A Case Study from the Linjiang Copper Mining Area, Jilin, China

Minerals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 407 ◽  
Author(s):  
Rongzhe Zhang ◽  
Tonglin Li ◽  
Shuai Zhou ◽  
Xinhui Deng
Keyword(s):  
Mining Area ◽  
Elastic Net ◽  
Gravity Inversion ◽  
Copper Mining ◽  
Structural Constraints ◽  
L1 Norm ◽  
Elastic Net Regularization ◽  
Gradient Constraints ◽  
Anomalous Bodies ◽  
Best Fit

We present a joint 2D inversion approach for magnetotelluric (MT) and gravity data with elastic-net regularization and cross-gradient constraints. We describe the main features of the approach and verify the inversion results against a synthetic model. The results indicate that the best fit solution using the L2 is overly smooth, while the best fit solution for the L1 norm is too sparse. However, the elastic-net regularization method, a convex combination term of L2 norm and L1 norm, can not only enforce the stability to preserve local smoothness, but can also enforce the sparsity to preserve sharp boundaries. Cross-gradient constraints lead to models with close structural resemblance and improve the estimates of the resistivity and density of the synthetic dataset. We apply the novel approach to field datasets from a copper mining area in the northeast of China. Our results show that the method can generate much more detail and a sharper boundary as well as better depth resolution. Relative to the existing solution, the large area divergence phenomenon under the anomalous bodies is eliminated, and the fine anomalous bodies boundary appeared in the smooth region. This method can provide important technical support for detecting deep concealed deposits.

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