Miao Li
ABSTRACT
The presence of contamination can influence the performance of parameter estimation in the binary logistic regression. Additionally, the emergence of collinearity among independent variables also gives rise to the issue of multicollinearity. In this work, we propose a novel correlation-driven adaptive lasso algorithm designed to enhance the robustness of logistic regression by incorporating a trimming step. The efficacy of this approach stems from the synergistic utilization of correlation-driven trimming techniques, which collectively serve to mitigate the impact of contaminated observations. The algorithm is designed to select information highly correlated features adaptively and to detect outilers simultaneously by maximizing a trimmed likelihood function. The proposed method has been evaluated and compared with other exisitng methods through a simulation study. Finally, an application to a real data set is given.
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Index Terms
- A Correlation-Driven Adaptive Lasso for Robust Logistic Regression Model Using Trimming Step
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