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  "Title": "OGA+HDIC+Trim and High-Dimensional Linear Regression Models",
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  "Author": "Hai-Tang Chiou, Ching-Kang Ing, Tze Leung Lai",
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  "Description": "Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a\nhigh-dimensional model selection procedure that comprises three\nsteps: orthogonal greedy algorithm (OGA), high-dimensional\ninformation criterion (HDIC), and Trim. The first two steps,\nOGA and HDIC, are used to sequentially select input variables\nand determine stopping rules, respectively. The third step,\nTrim, is used to delete irrelevant variables remaining in the\nsecond step. This package aims at fitting a high-dimensional\nlinear regression model via OGA+HDIC+Trim.",
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