Raw results for medium- and high-dimensional settings

NIAID Data Ecosystem2026-05-01 收录

高维变量选择

正则化回归方法

数据链接：

https://zenodo.org/record/8139858 数据链接链接失效反馈

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资源简介：

Raw results of the Best Subset Selection, Forward Stepwise, Lasso and Elastic-Net methods for medium- and high-dimensional settings.

创建时间：

2023-07-13

Variable selection in linear regressions with possibly all strongly correlated covariates

高维变量选择

资产定价模型

Penalized regression methods, particularly LASSO, require the irrepresentable condition for variable selection consistency. This condition places an upper bound on the magnitudes of correlation betwee

DataCite Commons2025-02-02 更新70

Combined-penalized likelihood estimations with a diverging number of parameters

高维变量选择

多重共线性

In the economics and biological gene expression study area where a large number of variables will be involved, even when the predictors are independent, as long as the dimension is high, the maximum s

Figshare2016-01-18 更新60

A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models

高维变量选择

经验贝叶斯方法

A new empirical Bayes approach to variable selection in the context of generalized linear models is developed. The proposed algorithm scales to situations in which the number of putative explanatory v

Figshare2019-12-19 更新30

Variable Selection with Second-Generation P-Values

高维变量选择

统计推断

Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here we report on a novel variable selection approach calle

NIAID Data Ecosystem60

Sparsity Oriented Importance Learning for High-Dimensional Linear Regression

高维变量选择

稀疏回归

With now well-recognized nonnegligible model selection uncertainty, data analysts should no longer be satisfied with the output of a single final model from a model selection process, regardless of it

Taylor & Francis Group2023-05-31 更新30

Raw results for medium- and high-dimensional settings

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