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- W3196609898 abstract "Over the last decades several regularization methods have been developed for sparse high-dimensional regression models. The influence of outliers is particularly awkward in the high dimensional context and so certain robust methods have been considered. Regularization methods simultaneously perform the model selection and the estimation of regression coefficients, merging a loss function based on the residuals and a penalty function inducing sparsity.Different penalties have been proposed, such as LASSO or Adaptive LASSO, a variant which improves the oracle model selection property, or non-concave penalties such as SCAD or MCP, which demostrably overcome the bias problem of the LASSO.We propose to examine robust losses with the various proposals for the penalties, leading to the differents estimating methods, namely the minimun density power divergence (DPD) and Renyi psedudodistance (RP) estimator penalized with LASSO, adaptative LASSO and SCAD. We develop an estimating algorithm for each method, focusing on their differences and similarities. Finally, we study the performance of the methods throught a simulation study." @default.
- W3196609898 created "2021-09-13" @default.
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- W3196609898 date "2021-08-28" @default.
- W3196609898 modified "2023-10-02" @default.
- W3196609898 title "A comparative study of robust regularization methods based on minimum density power and Rényi divergence losses" @default.
- W3196609898 hasPublicationYear "2021" @default.
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