Simulation Results

NIAID Data Ecosystem2026-05-01 收录

高维变量选择

稀疏建模

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https://zenodo.org/record/8027161 数据链接链接失效反馈

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资源简介：

Simulation results for the article "A neutral comparison of algorithms to minimize L0 penalties for high-dimensional variable selection" which is to be published by the Biometrical Journal.

创建时间：

2023-06-12

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

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

Results of the sparse group lasso optimisation—Values of penalised regression coefficients.

稀疏建模

高维特征选择

Results of the sparse group lasso optimisation—Values of penalised regression coefficients.

NIAID Data Ecosystem60

Precision Matrix Estimation With ROPE

协方差估计

稀疏建模

It is known that the accuracy of the maximum likelihood-based covariance and precision matrix estimates can be improved by penalized log-likelihood estimation. In this article, we propose a ridge-type

Figshare2017-01-06 更新40

Simulation Results

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