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  "Title": "A Tuning-Free Robust and Efficient Approach to High-Dimensional\nRegression",
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  "Authors@R": "c(person(given = \"Yunan\",family = \"Wu\", role = c(\"aut\", \"cre\",\"cph\"), email = \"yunan.wu@utdallas.edu\"), person(given = \"Lan\",family = \"Wang\", role = \"aut\") )",
  "Description": "Provide functions to estimate the coefficients in\nhigh-dimensional linear regressions via a tuning-free and\nrobust approach. The method was published in Wang, L., Peng,\nB., Bradic, J., Li, R. and Wu, Y. (2020), \"A Tuning-free Robust\nand Efficient Approach to High-dimensional Regression\", Journal\nof the American Statistical Association, 115:532,\n1700-1714(JASA’s discussion paper),\n<doi:10.1080/01621459.2020.1840989>. See also Wang, L., Peng,\nB., Bradic, J., Li, R. and Wu, Y. (2020), \"Rejoinder to “A\ntuning-free robust and efficient approach to high-dimensional\nregression\". Journal of the American Statistical Association,\n115, 1726-1729, <doi:10.1080/01621459.2020.1843865>; Peng, B.\nand Wang, L. (2015), \"An Iterative Coordinate Descent Algorithm\nfor High-Dimensional Nonconvex Penalized Quantile Regression\",\nJournal of Computational and Graphical Statistics, 24:3,\n676-694, <doi:10.1080/10618600.2014.913516>; Clémençon, S.,\nColin, I., and Bellet, A. (2016), \"Scaling-up empirical risk\nminimization: optimization of incomplete u-statistics\", The\nJournal of Machine Learning Research, 17(1):2682–2717; Fan, J.\nand Li, R. (2001), \"Variable Selection via Nonconcave Penalized\nLikelihood and its Oracle Properties\", Journal of the American\nStatistical Association, 96:456, 1348-1360,\n<doi:10.1198/016214501753382273>.",
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  "Date/Publication": "2024-01-29 18:34:30 UTC",
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