LASSO於羅吉斯迴歸模型之估計的應用

鍾其昀

Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/106389

DC Field	Value	Language
dc.contributor.advisor	薛慧敏	zh_TW
dc.contributor.author	鍾其昀	zh_TW
dc.creator	鍾其昀	zh_TW
dc.date	2017	en_US
dc.date.accessioned	2017-02-08T08:32:56Z	-
dc.date.available	2017-02-08T08:32:56Z	-
dc.date.issued	2017-02-08T08:32:56Z	-
dc.identifier	G0103354015	en_US
dc.identifier.uri	http://nccur.lib.nccu.edu.tw/handle/140.119/106389	-
dc.description	碩士	zh_TW
dc.description	國立政治大學	zh_TW
dc.description	統計學系	zh_TW
dc.description	103354015	zh_TW
dc.description.abstract	隨著資料量龐大，解釋變數過多的時代來臨，變數選取將是我們重要的議題。在線性迴歸分析中，傳統採用最小平方法(least square method)來估計模型，然而得到的迴歸係數估計值的偏差雖然比較小，但其變異程度卻較大，且預測得也不夠精準。若是考慮對迴歸係數加入限制式時，則估計量將與原本的最小平方法有何差異，偏差與標準差之間的比較。接著將此估計法應用至羅吉斯迴模型時，利用三筆實際資料，比較與最大概似估計(maximum likelihood estimate，簡稱MLE)法建立的迴歸模型及預測準確率，並於模擬實驗中，以表格及圖型呈現兩方法在估計量上的差異。	zh_TW
dc.description.tableofcontents	1. 緒論..............................................1\n2. 研究方法............................................3\n2.1 線性迴歸之LASSO....................................3\n2.2 羅吉斯迴歸之LASSO..................................14\n3. 實例資料分析........................................15\n3.1 脊椎後凸的預測.....................................15\n3.2 貓與狗影像的辨識...................................18\n3.3 鐵達尼號倖存者的預測................................21\n4. 模擬實驗...........................................24\n4.1 模擬流程與參數設計..................................24\n4.2 估計量的比較.......................................25\n5. 結論.............................................49\n參考文獻..............................................50\n附錄一(2.3)之推導證明..................................51\n附錄二(2.3)之推導證明..................................52	zh_TW
dc.format.extent	934999 bytes	-
dc.format.mimetype	application/pdf	-
dc.source.uri	http://thesis.lib.nccu.edu.tw/record/#G0103354015	en_US
dc.subject	最小平方法	zh_TW
dc.subject	最大概似估計	zh_TW
dc.title	LASSO於羅吉斯迴歸模型之估計的應用	zh_TW
dc.title	Application of LASSO Estimation of a Logistic Regression Model	en_US
dc.type	thesis	en_US
dc.relation.reference	一、英文文獻\n1. Boyd, S. and Vandenberghe, L. (2004), Convex Optimization, Cambridge University Press, 215-244.\n2. Breiman, L. (1995) Better Subset Regression Using the Nonnegative Garrotte, American Statistical Association, 37, 373-384.\n3. Breiman, L. and Spector P. (1992) Submodel Selection and Evaluation in Regression. The X-Random Case,\nInternational Statistical Review, 60, 291-319.\n4. Dalal, N. Triggs, B. (2005) Histograms of Oriented Gradients for Human Detection, http://lear.inrialpes.fr/.\n5. Friedl, I., Tilg, N. (1995) Variance estimates in logistic regression using the bootstrap, Communications in Statistic-Theory and Methods, 24(2), 473-486.\n6. Hoerl, E. and Kennard, R. (1970) Ridge Regression: Biased Estimation for Nonorthogonal Problems, American Statistical Association, 12, 55-67.\n7. Osborne, M., Presnell, B. and Turlach, B. (2000) On the LASSO and its dual, Journal of Computational and Graphical Statistics, 9, 319–337.\n8. Sill, M.,Hielscher, T. A and Becker M. (2014) Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models, Journal of Statistical Software, 62, 1-22.\n9. Tibshirani, R. (1996) Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society, 58, 267-288.\n10. Zhao, X. (2008) Lasso and Its Applications, University of Minnesota Duluth, 4-17.\n11. Zou, H. and Hastie, T. (2005) Regularization and Variable Selection via the Elastic Net, Journal of the Royal Statistical Society, 67, 301-320.\n\n二、中文文獻\n1. 全民人體力學健康教室，淺談三種脊椎歪斜。\n2. 賈金柱，高等統計選講，高等統計入門分析，2.2 節Duality。	zh_TW
item.fulltext	With Fulltext	-
item.grantfulltext	open	-
item.openairecristype	http://purl.org/coar/resource_type/c_46ec	-
item.cerifentitytype	Publications	-
item.openairetype	thesis	-
Appears in Collections:	學位論文

Files in This Item:

File	Description	Size	Format
401501.pdf		913.08 kB	Adobe PDF2	View/Open

Show simple item record

Google Scholar^TM

Check

Files in This Item:

Google ScholarTM

Google Scholar^TM