http://article.sapub.org/10.5923.j.ajms.20240702.03.html WebJan 30, 2024 · With the continuous application of spatial dependent data in various fields, spatial econometric models have attracted more and more attention. In this paper, a robust variable selection method based on exponential squared loss and adaptive lasso is proposed for the spatial Durbin model. Under mild conditions, we establish the asymptotic …
Robust Bayesian model selection for variable clustering with the ...
WebAbstract: The adaptive least absolute shrinkage and selection operator (Lasso) and least absolute deviation (LAD)-Lasso are two attractive shrinkage methods for simultaneous … WebAug 28, 2024 · Robust Adaptive Lasso method for parameter's estimation and variable selection in high-dimensional sparse models PLoS One. 2024 Aug 28;12 (8):e0183518. … cpt right knee orif
robust and efficient variable selection method for linear …
WebLASSO is inconsistent, and the oracle property does not hold. Zou (2006) proposed the adaptive LASSO, and showed that it enjoys the oracle property. The adaptive LASSO penalty: p nj (j jj) = nj j jj, nj = ˝ nj=j ~ jjk for some k >0, where ~ = ( ~ 1; ; ~ d)T is a p n-consistent estimator of 0, and ˝ nj’s are the regularization parameters. WebTo make the bias reduction feasible, we introduce the adaptive robust Lasso (AR-Lasso). The AR-Lasso first runs R-Lasso to obtain an initial estimate, and then computes the weight vector of the weighted L1-penalty according to a de-creasing function of the magnitude of the initial estimate. After that, AR-Lasso runs WR-Lasso with the computed ... WebFeb 4, 2024 · This paper studies the outlier detection and robust variable selection problem in the linear regression model. The penalized weighted least absolute deviation (PWLAD) regression estimation method and the adaptive least absolute shrinkage and selection operator (LASSO) are combined to simultaneously achieve outlier detection, and robust … cpt right neck mass