Robust variable selection
WebFollowing the research line in [12,19], we consider a new robust variable selection method by integrating the issues of modal regression (for estimating the conditional mode function) and variable screening based on functional derivatives. To the best of our knowledge, this is the first paper to address robust model-free variable selection. WebApr 12, 2024 · Variable selection plays an essential role in regression analysis as it identifies important variables that are associated with outcomes and is known to improve predictive accuracy of resulting ...
Robust variable selection
Did you know?
WebApr 12, 2024 · Sparse penalized quantile regression is a useful tool for variable selection, robust estimation, and heteroscedasticity detection in high-dimensional data analysis. The computational issue of the ... WebJan 17, 2024 · We develop a robust Bayesian variable selection method for G×E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both
WebJun 20, 2024 · Rank regression is a robust modeling tool; it is challenging to implement it for the distributed massive data owing to memory constraints. In practice, the massive data may be distributed heterogeneously from machine to machine; how to incorporate the heterogeneity is also an interesting issue. This paper proposes a distributed rank … WebFeb 25, 2024 · We propose a new robust variable selection procedure for FMR models. The proposed method is based on minimum-distance techniques, which seem to have some …
WebMay 22, 2012 · To make the WR-Lasso practically feasible, we propose a two-step procedure, called adaptive robust Lasso (AR-Lasso), in which the weight vector in the … WebAug 31, 2024 · MIProb: Robust Variable Selection under the Logistic Slippage Model We consider a two-class logistic regression model affected by data contamination (i.e., outliers) and comprising irrelevant covariates. Specifically, we focus on the logistic slippage model, where the number, position and strength of the outliers are unknown [ 14, 43 ].
WebHuang's these variable selection procedures has not been well character research is partially supported by a funding through Projet 21 i Phase 3 of Shang- ized or understood. For …
WebSep 15, 2024 · Here we consider the problem of scalable, robust variable selection in DNNs for the identification of putative causal genetic variants in genome sequencing studies. We identified a pronounced ... finns point lighthouse njWebSelecting explanatory variables has become one of the most important tasks in statistics. However, many of existing variable selection methods are sensitive to outliers. To address this issue, we develop a class of robust linear regression estimators, namely, penalized maximum tangent likelihood estimation. esporta fitness west dundeeWebJul 2, 2014 · We propose a penalized robust estimator for both the regression coefficients and the variance of random effects based on a re-parametrization of the linear mixed models. Under some regularity conditions, we show the oracle properties of the proposed robust variable selection method. Simulation study shows the robustness of the … finns rd kelownaWebIn order to ensure a high probability of correct selection and efficient parameter estimation, we investigate a robust variable selection method based on a modified Huber's function with an exponential squared loss tail. We also prove … esportal cheatsWebJul 1, 2016 · We give a model structure identification and variable selection method. • It can be easily implemented in one step. • It has advantages in robustness and efficiency. • … esporta hilliard shootingWebFeb 1, 2015 · This paper presents a new efficient and robust smooth-threshold generalized estimating equations for generalized linear models (GLMs) with longitudinal data.The proposed method is based on a bounded exponential score function and leverage-based weights to achieve robustness against outliers both in the response and the covariate … finns rec clubWebMay 14, 2014 · The varying coefficient model is widely used as an extension of the linear regression model. Many procedures have been developed for the model estimation, and recently efficient variable selection procedures for the varying coefficient model have been proposed as well. However, those variable selection approaches are mainly built on the … esportal gulfpower.com