HDCI

R package HDCI: High Dimensional Confidence Interval Based on Lasso and Bootstrap. Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.

Keywords for this software

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References in zbMATH (referenced in 2 articles , 1 standard article )

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Sorted by year (citations)

  1. Liu, Hanzhong; Xu, Xin; Li, Jingyi Jessica: A bootstrap Lasso + partial ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models (2020)
  2. van Erp, Sara; Oberski, Daniel L.; Mulder, Joris: Shrinkage priors for Bayesian penalized regression (2019)