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| 研究生： |
朱晉楠 Chu,Chin Nan |
|---|---|
| 論文名稱： |
LASSO與廣義LASSO選取變數比較 A comparative study of lasso and a general version of lasso for variable selection |
| 指導教授： |
黃子銘
Huang,Tzee Ming |
| 口試委員: |
黃子銘
翁久幸 黃貞瑛 |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 統計學系 Department of Statistics |
| 論文出版年： | 2017 |
| 畢業學年度： | 105 |
| 語文別： | 中文 |
| 論文頁數： | 19 |
| 中文關鍵詞： | 變數選取 、最小絕對值壓縮挑選運算 、貝式訊息準則 |
| 外文關鍵詞： | Variable selection, Least absolute shrinkage and selection operator, Bayesian information criterion |
| 相關次數： | 點閱：257 下載：22 |
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在建構模型時，變數的選取是非常重要的，一般使用向前選取、向後刪除、逐步迴歸來挑選變數。
Tibshirani[4]在1996 年提出最小絕對值壓縮挑選運算least absolute
shrinkage and selection operator；簡稱LASSO），LASSO 方法結合了變數係數的壓縮與變數選取。
本研究針對 LASSO 的限制式做修改，另外也將搜尋參數t 的方法改良，評估統計模型優劣則使用貝氏訊息準則，最後，改良的搜尋方法能更精確找到對於反應變數有影響的解釋變數，達到選取變數的效果。
In model construction, variable selection is a very important issue.Typical variable selection tools include
forward selection, backward selection and stepwise selection. In 1996,Tibshirani proposed a method called LASSO (Least Absolute Shrinkage and Selection Operator), which can be used for variable selection via
coefficient shrinkage.
In this thesis, a general version of LASSO is proposed to improve the variable selection ability of LASSO. The proposed method is obtained by modifiying the constraints of LASSO. For both LASSO and the proposed method, the constraints depends on a shrinkage parameter that needs to be specified. In this thesis, the shrinkage parameter is selected using Bayesian information criterion. When the optimal parameter is found, the proposed method outperforms LASSO in variable selection. However, the search of the optimal parameter can be computationally intensive.
第一章 緒論 1
第二章 文獻探討 3
第三章 研究方法 7
第四章 模擬資料分析 11
第五章 結論與建議 18
參考文獻 19
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