| dc.contributor.advisor | 黃子銘 | zh_TW |
| dc.contributor.advisor | Huang,Tzee Ming | en_US |
| dc.contributor.author (Authors) | 朱晉楠 | zh_TW |
| dc.contributor.author (Authors) | Chu,Chin Nan | en_US |
| dc.creator (作者) | 朱晉楠 | zh_TW |
| dc.creator (作者) | Chu, Chin Nan | en_US |
| dc.date (日期) | 2017 | en_US |
| dc.date.accessioned | 31-Jul-2017 12:53:54 (UTC+8) | - |
| dc.date.available | 31-Jul-2017 12:53:54 (UTC+8) | - |
| dc.date.issued (上傳時間) | 31-Jul-2017 12:53:54 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0104354030 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/111598 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計學系 | zh_TW |
| dc.description (描述) | 104354030 | zh_TW |
| dc.description.abstract (摘要) | 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 absoluteshrinkage 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。 | zh_TW |
| dc.description.abstract (摘要) | In model construction, variable selection is a very important issue.Typical variable selection tools includeforward 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 viacoefficient 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. | en_US |
| dc.description.tableofcontents | 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 | zh_TW |
| dc.format.extent | 935963 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0104354030 | en_US |
| dc.subject (關鍵詞) | 變數選取 | zh_TW |
| dc.subject (關鍵詞) | 最小絕對值壓縮挑選運算 | zh_TW |
| dc.subject (關鍵詞) | 貝式訊息準則 | zh_TW |
| dc.subject (關鍵詞) | Variable selection | en_US |
| dc.subject (關鍵詞) | Least absolute shrinkage and selection operator | en_US |
| dc.subject (關鍵詞) | Bayesian information criterion | en_US |
| dc.title (題名) | LASSO與廣義LASSO選取變數比較 | zh_TW |
| dc.title (題名) | A comparative study of lasso and a general version of lasso for variable selection | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | Yoav Benjamini and Yosef Hochberg. Controlling the false discovery rate:a practical and powerful approach to multiple testing. Journal of theRoyal Statistical Society, Series B, 57(1), 1995.Akaike Hirotsugu. A new look at the statistical model identifation.IEEE Transactions on Automatic Control, 19(6), 1974.M.R. Osborne, B. Presnell, and B.A. T urlach. A note on the leastabsolute shrink age and selection operator. unpublished manuscript. 1998.Tibshirani Robert. Regression shrinkage and selection via the lasso. Jour-nal of the Royal Statistical Society., 1996.Gideon E Schwarz. Estimating the dimension of a model. Annals ofStatistics, 6(2), 1978.Ming Yuan and Yi Lin. Model selection and estimation in regressionwith grouped variables. Journal of the Royal Statistical Society, SeriesB, 68(1), 2006.Hui Zou and Hastie. Regularization and variable selection via the elasticnet. Journal of the Royal Statistical Society, Series B, 2005. | zh_TW |
