QuantRegGLasso

Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models

CRAN Package

Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies. (doi:10.3150/18-BEJ1091).


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Insights

Last 30 days

This package has been downloaded 142 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

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0 downloadsMar 2, 2025
6 downloadsMar 3, 2025
8 downloadsMar 4, 2025
7 downloadsMar 5, 2025
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5 downloadsApr 1, 2025
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0 downloadsApr 5, 2025
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The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 1,737 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 20 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Data provided by CRAN


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Dependencies

  • Imports2 packages
  • Suggests3 packages
  • Linking To2 packages