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| Field | Value |
|---|---|
| Title: | 模糊樣本之區間迴歸分析 |
| Authors: | 陳孝煒 |
| Contributors: | 吳柏林 陳孝煒 |
| Keywords: | 模糊迴歸參數區間估計 最小平方法 區間模糊數距離 |
| Date: | 2006 |
| Issue Date: | 2009-09-11 16:01:55 (UTC+8) |
| Description.abstract: | 傳統的迴歸是假設觀測值的不確定性來自於隨機，模糊迴歸則是假設不確定性來自多重隸屬現象。一般的模糊迴歸採用樣本模糊數 來對模糊迴歸參數進行估計，其中 為觀測模糊數， 依舊為實數值。我們認為 的假設不能真實地表達出樣本所蘊含的資訊，本研究將假設 也為模糊數，如此一來對樣本的解釋方式將更為貼近現實，且估計的過程則採用通用的最小平方估計，保留迴歸原始精神但是在模糊數上則有更深入的探究。迴歸常用來建構經濟和財務的模型，而此種模型經常帶有模糊的特質，例如景氣循環、不規則趨勢等。在本文中也會舉出例子來輔助說明此研究的實用性。
關鍵字：模糊迴歸參數區間估計、最小平方法、區間模糊數距離 |
| Reference: | [1]吳柏林，（1999）。現代統計學，252-255。台北：五南書局 [2]吳柏林¸楊文山 (1997). 模糊統計在社會調查分析的應用. 社會科學計量方法發展與應用. 楊文山主編:中央研究院中山人文社會科學研究所. [3]吳柏林(2005).模糊統計導論方法與應用。台北，五南圖書出版社。 [4]陳雲岫,蔡敏盛,(1998)，模糊迴歸分析穩健性之探討- 以二階線性為例.中國工業工程學會論文集,中華民國八十七年度,pp.1034-1039,1998年12月. [5]阮亨中、吳柏林，（2000）。模糊數學與統計應用， 233-250; 319-341。台北：俊傑書局。 [6]Wu, B. and Tseng, N. (2002). A new approach to fuzzy regression models with application to business cycle analysis. Fuzzy Sets and System. 130,33-42. [7]Yang, M. and Ko, C. (1997). On cluster-wise fuzzy regression analysis. IEEE Trans. Systems Man Cybernet, vol27,1-13. [8]Savic, D.A. and Pedrycz, W. (1991). Evaluation of Fuzzy Linear Regression Models. Fuzzy Set and Systems, 23, 51-63. [9]Tanaka, H., Uejima, S. and Asai, K. (1980). Fuzzy Linear Regression Model. International Congress on Applied Systems Research and Cybernetics. Aculpoco, Mexico. [10]Tanaka, H., Uejima, S. and Asai, K. (1982). Linear Regression Analysis with Fuzzy model. IEEE Trans. SystemsMan Cybernet, vol SMC 12, 903-907. [11]Tanaka, H., & Ishibuchi, H. (1993). An architecture of neural networks with interval weights and its application to fuzzy regression analysis. Fuzzy Sets and Systems, 57, 27-39. [12] Wang, H. F. and R. C. Tsaur, “Bi-criteria variable selection in fuzzy regression equation,” Computers and Mathematics with Applications, 40, 877-883(2000).. [13] Tanaka, H. and J. Watada, “Possibility linear systems and their application to the linear regression model,”Fuzzy Sets and Systems, 27, 275-289 (1988). [14] H. Tanaka, S. Vejima, K. Asai, Linear regression analysis with fuzzy model, IEEE Trans. Syst. Man, Cybernetics, Jun, 1982. [15] P. Diamond . Fuzzy least squares. Information Science 46 (1998) |
| Description: | 碩士 國立政治大學 應用數學研究所 93751014 95 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0093751014 |
| Data Type: | thesis |
| DCField | Value | Language |
|---|---|---|
| dc.contributor.advisor | 吳柏林 | zh_TW |
| dc.contributor.author | 陳孝煒 | zh_TW |
| dc.creator (Authors) | 陳孝煒 | zh_TW |
| dc.date (Date) | 2006 | en_US |
| dc.date.accessioned | 2009-09-11 16:01:55 (UTC+8) | - |
| dc.date.available | 2009-09-11 16:01:55 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2009-09-11 16:01:55 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0093751014 | en_US |
| dc.identifier.uri (URI) | http://nccuir.lib.nccu.edu.tw/handle/140.119/29675 | - |
| dc.description (Description) | 碩士 | zh_TW |
| dc.description (Description) | 國立政治大學 | zh_TW |
| dc.description (Description) | 應用數學研究所 | zh_TW |
| dc.description (Description) | 93751014 | zh_TW |
| dc.description (Description) | 95 | zh_TW |
| dc.description.abstract (Abstract) | 傳統的迴歸是假設觀測值的不確定性來自於隨機，模糊迴歸則是假設不確定性來自多重隸屬現象。一般的模糊迴歸採用樣本模糊數 來對模糊迴歸參數進行估計，其中 為觀測模糊數， 依舊為實數值。我們認為 的假設不能真實地表達出樣本所蘊含的資訊，本研究將假設 也為模糊數，如此一來對樣本的解釋方式將更為貼近現實，且估計的過程則採用通用的最小平方估計，保留迴歸原始精神但是在模糊數上則有更深入的探究。迴歸常用來建構經濟和財務的模型，而此種模型經常帶有模糊的特質，例如景氣循環、不規則趨勢等。在本文中也會舉出例子來輔助說明此研究的實用性。
關鍵字：模糊迴歸參數區間估計、最小平方法、區間模糊數距離 | zh_TW |
| dc.description.tableofcontents | 第1章 前言…………………………………………………………..2
第2章 模糊回歸的架構……………………………………………..3 第3章 模糊迴歸模式的參數估計…………………………………..4 第4章 性質…………………………………………………………..10 第5章 推廣…………………………………………………………..13 第6章 實例探討……………………………………………………..15 第7章 結論…………………………………………………………..18 參考文獻……………………………………………………………....20 附註……………………………………………………………..……..21 註1樣本擴大t倍之證明………………………….…………………21 註2樣本平移t單位之證明………………………….………………22 註3實例探討的圖表和數據完整推導………………...…………….24 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (Source URI) | http://thesis.lib.nccu.edu.tw/record/#G0093751014 | en_US |
| dc.subject (Keywords) | 模糊迴歸參數區間估計 | zh_TW |
| dc.subject (Keywords) | 最小平方法 | zh_TW |
| dc.subject (Keywords) | 區間模糊數距離 | zh_TW |
| dc.title (Title) | 模糊樣本之區間迴歸分析 | zh_TW |
| dc.type (Data Type) | thesis | en |
| dc.relation.reference (Reference) | [1]吳柏林，（1999）。現代統計學，252-255。台北：五南書局 | zh_TW |
| dc.relation.reference (Reference) | [2]吳柏林¸楊文山 (1997). 模糊統計在社會調查分析的應用. 社會科學計量方法發展與應用. 楊文山主編:中央研究院中山人文社會科學研究所. | zh_TW |
| dc.relation.reference (Reference) | [3]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 |
| dc.relation.reference (Reference) | [4]陳雲岫,蔡敏盛,(1998)，模糊迴歸分析穩健性之探討- 以二階線性為例.中國工業工程學會論文集,中華民國八十七年度,pp.1034-1039,1998年12月. | zh_TW |
| dc.relation.reference (Reference) | [5]阮亨中、吳柏林，（2000）。模糊數學與統計應用， 233-250; 319-341。台北：俊傑書局。 | zh_TW |
| dc.relation.reference (Reference) | [6]Wu, B. and Tseng, N. (2002). A new approach to fuzzy regression models with application to business cycle analysis. Fuzzy Sets and System. 130,33-42. | zh_TW |
| dc.relation.reference (Reference) | [7]Yang, M. and Ko, C. (1997). On cluster-wise fuzzy regression analysis. IEEE Trans. Systems | zh_TW |
| dc.relation.reference (Reference) | Man Cybernet, vol27,1-13. | zh_TW |
| dc.relation.reference (Reference) | [8]Savic, D.A. and Pedrycz, W. (1991). Evaluation of Fuzzy Linear Regression Models. Fuzzy Set and Systems, 23, 51-63. | zh_TW |
| dc.relation.reference (Reference) | [9]Tanaka, H., Uejima, S. and Asai, K. (1980). Fuzzy Linear Regression Model. International Congress on Applied Systems Research and Cybernetics. Aculpoco, Mexico. | zh_TW |
| dc.relation.reference (Reference) | [10]Tanaka, H., Uejima, S. and Asai, K. (1982). Linear Regression Analysis with Fuzzy model. IEEE Trans. SystemsMan Cybernet, vol SMC 12, 903-907. | zh_TW |
| dc.relation.reference (Reference) | [11]Tanaka, H., & Ishibuchi, H. (1993). An architecture of neural networks with interval weights and its application to fuzzy regression analysis. Fuzzy Sets and Systems, 57, 27-39. | zh_TW |
| dc.relation.reference (Reference) | [12] Wang, H. F. and R. C. Tsaur, “Bi-criteria variable selection in fuzzy regression equation,” Computers and Mathematics with Applications, 40, 877-883(2000).. | zh_TW |
| dc.relation.reference (Reference) | [13] Tanaka, H. and J. Watada, “Possibility linear systems and their application to the linear regression model,”Fuzzy Sets and Systems, 27, 275-289 (1988). | zh_TW |
| dc.relation.reference (Reference) | [14] H. Tanaka, S. Vejima, K. Asai, Linear regression analysis with fuzzy model, IEEE Trans. Syst. Man, Cybernetics, Jun, 1982. | zh_TW |
| dc.relation.reference (Reference) | [15] P. Diamond . Fuzzy least squares. Information Science 46 (1998) | zh_TW |
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