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  "Title": "Group Variable Selection for Quantile and Robust Mean Regression",
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  "Authors@R": "c(person(\"Shaobo\", \"Li\", email=\"shaobo.li@ku.edu\", role=c(\"aut\", \"cre\")),\nperson(\"Ben\", \"Sherwood\", email=\"ben.sherwood@ku.edu\", role=c(\"aut\")))",
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  "Description": "A program that conducts group variable selection for\nquantile and robust mean regression (Sherwood and Li, 2022).\nThe group lasso penalty (Yuan and Lin, 2006) is used for\ngroup-wise variable selection. Both of the quantile and mean\nregression models are based on the Huber loss. Specifically,\nwith the tuning parameter in the Huber loss approaching to 0,\nthe quantile check function can be approximated by the Huber\nloss for the median and the tilted version of Huber loss at\nother quantiles. Such approximation provides computational\nefficiency and stability, and has also been shown to be\nstatistical consistent.",
  "URL": "GitHub: https://github.com/shaobo-li/hrqglas",
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  "Repository": "https://shaobo-li.r-universe.dev",
  "Date/Publication": "2023-02-28 20:44:03 UTC",
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