| dc.contributor.advisor | 宋傳欽 | zh_TW |
| dc.contributor.author (作者) | 趙家慶 | zh_TW |
| dc.creator (作者) | 趙家慶 | zh_TW |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 17-九月-2009 13:46:45 (UTC+8) | - |
| dc.date.available | 17-九月-2009 13:46:45 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-九月-2009 13:46:45 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0093751003 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/32575 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 應用數學研究所 | zh_TW |
| dc.description (描述) | 93751003 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 使用傳統迴歸的方式對未知事物做預測，往往不能夠精準的做出結論，縱使在相同的條件下實際去操作，也很難得到相同的結果，因此模糊數概念的建立，並運用在迴歸分析上更能有效描述預測結果的不確定性。然而模糊線性迴歸(Fuzzy Linear Regression)在利用最小平方法處理問題時，往往過於著重在模糊區間的中心與分展度上，而忽略了描述資料的模糊性，使得隸屬度函數(membership function)的功能受到相當大的限制。本文在D`Urso和Gastaldi(2000)所提出的雙重模糊線性迴歸(doubly fuzzy linear regression)模型架構下，利用Yang和Ko(1996)在LR空間下所定義模糊數間的距離公式，導出能反映隸屬度函數的最小平方估計，並引進一些傳統迴歸中常用來偵測離群值(outlier)與具影響力觀察值(influence observation)的概念與技巧，應用在模糊線性迴歸資料的偵測上。 | zh_TW |
| dc.description.tableofcontents | 誌謝1 序論1.1摘要1.2簡介2 模糊線性迴歸的介紹2.1模糊數及其運算2.2一般模糊線性迴歸模型2.3簡單距離公式2.4對稱和不對稱雙重模糊線性迴歸模型3 LR型模糊線性迴歸3.1LR型模糊數3.2Yang和Ko距離公式3.3Yang和Ko距離公式下之最小平方估計4 具影響力觀察值之偵測4.1傳統線性迴歸中的偵測方法4.2模糊線性迴歸中的偵測方法5 實例分析6 附錄6.1 附錄一6.2 附錄二6.3 附錄三6.4 附錄四參考書目 | zh_TW |
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| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0093751003 | en_US |
| dc.subject (關鍵詞) | 模糊線性迴歸 | zh_TW |
| dc.subject (關鍵詞) | 具影響力觀察值 | zh_TW |
| dc.subject (關鍵詞) | 離群值 | zh_TW |
| dc.subject (關鍵詞) | 雙重模糊線性迴歸模型 | zh_TW |
| dc.subject (關鍵詞) | 隸屬度函數 | zh_TW |
| dc.subject (關鍵詞) | fuzzy linear regression | en_US |
| dc.subject (關鍵詞) | influence observation | en_US |
| dc.subject (關鍵詞) | outlier | en_US |
| dc.subject (關鍵詞) | doubly fuzzy linear regression | en_US |
| dc.subject (關鍵詞) | membership function | en_US |
| dc.title (題名) | 模糊線性迴歸之研究 | zh_TW |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | [1]Draper, N. R. and Smith, H., (1980). Applied Regression Analysis,Wiley, New York. | zh_TW |
| dc.relation.reference (參考文獻) | [2]D`Urso, P. and Gastaldi, T., (2000). A least-squares approach to fuzzy linear regression analysis. 34, 427-440. | zh_TW |
| dc.relation.reference (參考文獻) | [3]D`Urso, P., (2003). Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data. 42,47-72. | zh_TW |
| dc.relation.reference (參考文獻) | [4]Tanaka, H., (1987). Fuzzy data analysis by possibilistic linear models. | zh_TW |
| dc.relation.reference (參考文獻) | [5]Tanaka, H., Uejima, S., Asai, K., (1982). Fuzzy limear regression model.903-907. | zh_TW |
| dc.relation.reference (參考文獻) | [6]Xu, R. and Li, C., (2001). Multidimensional least-squares fitting with a fuzzy model.215-223. | zh_TW |
| dc.relation.reference (參考文獻) | [7]Yang, M. S. and Ko, C. H., (1996). On a class of $c$-numberrs clustering procedures for fuzzy data.84,49-60. | zh_TW |
| dc.relation.reference (參考文獻) | [8]Yang, M. S. and Liu, H. H., (2003). Fuzzy least-squares algorithms for interactive fuzzy linear regression modles.135, 305-316. | zh_TW |
| dc.relation.reference (參考文獻) | [9]Yang, M. S. and Liu, H. H., (2005). A new statistic for influence in linear regression.47, 305-316 | zh_TW |
| dc.relation.reference (參考文獻) | [10]Zimmermann, H. J., (1991). Fuzzy Set Theory and its Applications,Kluwer,Dordrecht. | zh_TW |
| dc.relation.reference (參考文獻) | [11]吳柏林(2005)：模糊統計導論方法與應用。台北，五南圖書出版社。 | zh_TW |
