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  "Title": "Adaptively Weighted Group Lasso for Semiparametric Quantile\nRegression Models",
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  "Description": "Implements an adaptively weighted group Lasso procedure\nfor simultaneous variable selection and structure\nidentification in varying coefficient quantile regression\nmodels and additive quantile regression models with ultra-high\ndimensional covariates. The methodology, grounded in a strong\nsparsity condition, establishes selection consistency under\ncertain weight conditions. To address the challenge of tuning\nparameter selection in practice, a BIC-type criterion named\nhigh-dimensional information criterion (HDIC) is proposed. The\nLasso procedure, guided by HDIC-determined tuning parameters,\nmaintains selection consistency. Theoretical findings are\nstrongly supported by simulation studies. (Toshio Honda,\nChing-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).",
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