The trimmed lasso: sparsity and robustness
WebThe sparse least trimmed squares (sLTS) is a sparse version of the well-known robust linear regression method LTS based on the trimmed loss function with L 1 regularization. Recently, the robust parameter estimation using density power weight has been discussed by Windham [ 6 ], Basu et al. [ 7 ], Jones et al. [ 8 ], Fujisawa and Eguchi [ 9 ], Basu et al. [ 10 ], … Webtuning parameter and their implementation are paramount to the robustness and e ciency of variable selection. This work proposes a penalized robust variable selection method for multiple linear regression through the least trimmed squares loss function. The proposed method employs a robust tuning parameter criterion constructed through BIC for ...
The trimmed lasso: sparsity and robustness
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WebNov 9, 2024 · Modern statistical learning algorithms are capable of amazing flexibility, but struggle with interpretability. One possible solution is sparsity: making inference such that … WebThe Trimmed Lasso: Sparsity and Robustness. Click To Get Model/Code. Nonconvex penalty methods for sparse modeling in linear regression have been a topic of fervent …
WebThe first result of this paper is that the solution to Lasso has robustness properties: it is the solution to a robust optimiza-tion problem. In itself, this interpretation of Lasso as the solu-tion to a robust least squares problem is a development in line with the results of [13]. There, the authors propose an alterna- WebApr 11, 2024 · The biomarker development field within molecular medicine remains limited by the methods that are available for building predictive models. We developed an efficient method for conservatively estimating confidence intervals for the cross validation-derived prediction errors of biomarker models. This new method was investigated for its ability to …
WebThe sparse LTS (1.4) can also be interpreted as a trimmed version of the lasso, since the limit case h =n yields the lasso solution. Other robust versions of the lasso have been considered in the literature. Most of them are penalized M-estimators, as in van de Geer (2008) and Li, Peng and Zhu (2011). WebMay 11, 2024 · Outlier detection has become an important and challenging issue in high-dimensional data analysis due to the coexistence of data contamination and high-dimensionality. Most existing widely used penalized least squares methods are sensitive to outliers due to the l2 loss. In this paper, we proposed a Robust Moderately Clipped LASSO …
WebWe have taken care to normalize the different penalty functions so that µ is the sparsity parameter and γ corresponds to the approximation of the indicator I{ β > 0}. For SCAD, it …
WebApr 12, 2024 · Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso. October 2015. Eunho Yang ... In this paper, we propose the Trimmed Graphical Lasso for robust estimation of sparse GGMs. promis meanWebAbstract In high-dimensional data analysis, we often encounter partly sparse and dense signals or parameters. Considering an l q-penalization with different qs for each sub … promis measures for childrenWebRobust Gaussian Graphical Modeling with the Trimmed Graphical Lasso Eunho Yang, Aurelie C. Lozano; Parallelizing MCMC with Random Partition Trees Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson; Convergence rates of sub-sampled Newton methods Murat A. Erdogdu, Andrea Montanari promis manufacturingWebThe classical lasso estimation for sparse, high-dimensional regression models is typically biased and lacks the oracle properties. The desparsified versions of the lasso have been … promis measures pediatricWebJul 4, 2024 · The Trimmed Lasso: Sparsity and Robustness. 1 code implementation • 15 Aug 2024 • Dimitris Bertsimas, Martin S. Copenhaver , Rahul Mazumder. Nonconvex penalty methods for sparse modeling in linear ... promis measures anxietyWeb2) Further, in relating the trimmed Lasso to commonly used sparsity-inducing penalty functions, we provide a succinct characterization of the connection between trimmed … promis merrymanWebJun 14, 2010 · Robust Regression and Lasso. Abstract: Lasso, or l 1 regularized least squares, has been explored extensively for its remarkable sparsity properties. In this … labor pascher