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Selection of Smoothing Parameter for One-Step Sparse Estimates with Lq Penalty
並列摘要
This paper discusses the selection of the smoothing parameter necessary to implement a penalized regression using a nonconcave penalty function. The proposed method can be derived from a Bayesian viewpoint, and the resultant smoothing parameter is guaranteed to satisfy the sufficient conditions for the oracle properties of a one-step estimator. The results of simulation and application to some real data sets reveal that our proposal works efficiently, especially for discrete outputs.
被引用紀錄
周書仲（2007）。幾丁聚醣選擇性接枝聚氮-異丙基丙烯醯胺的合成及其酸鹼/溫度敏感性質研究〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2007.00979
廖國淳（2013）。聚乳酸成形蜂巢結構薄膜與電漿輔助改質表面研究〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2013.00661
張原譯（2014）。風力發電機葉片於颱風風況下之結構強度分析〔碩士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU.2014.01578
延伸閱讀
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