Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/110693| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 吳柏林 | zh_TW |
| dc.contributor.author | 陳帥 | zh_TW |
| dc.creator | 陳帥 | zh_TW |
| dc.date | 2017 | en_US |
| dc.date.accessioned | 2017-07-03T06:41:05Z | - |
| dc.date.available | 2017-07-03T06:41:05Z | - |
| dc.date.issued | 2017-07-03T06:41:05Z | - |
| dc.identifier | G0104751018 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/110693 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 應用數學系 | zh_TW |
| dc.description | 104751018 | zh_TW |
| dc.description.abstract | 目標：本文旨在建構一種新型的模糊回歸模式，解決一类較複雜的模糊回歸問題。\n研究方法:推廣局部加權回歸的思想，先從理論上構建新模型；然後借由模拟數據，從多個方面考察新模型的性質，并和其他模型做比較。\n發現：局部加權回歸方法結合模糊隸屬度概念，使模糊回歸理論有更多的應用場合。\n原創性：目前在模糊回歸領域的主流思想是通過線性規劃等方法來構建模型，而本文另闢蹊徑，首次從局部加權的角度構建了模糊回歸的新模型。 | zh_TW |
| dc.description.abstract | Objective: This paper aims to construct a new fuzzy regression model to solve a more complex fuzzy regression problem.\nMethod: Build a new model by promoting the idea of locally weighted regression; Using simulated data to compare the new model with other models.\nConclusion: The fuzzy membership degree concept combined with the locally weighted regression method makes the fuzzy regression theory have more applications.\nOriginality: At present, the main idea in the field of fuzzy regression is to construct models by means of linear programming. In this paper, a new model of fuzzy regression is constructed from the perspective of locally weighted method for the first time. | en_US |
| dc.description.tableofcontents | 1.前言 1\n2．模糊數據的局部加權回歸 5\n2.1 模型的建構 5\n2.2 回歸係數的估計 6\n2.3 殘差分析 7\n2.4 數據模擬 8\n3.實證分析 12\n4.結語 18\n參考文獻 19 | zh_TW |
| dc.format.extent | 1396648 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0104751018 | en_US |
| dc.subject | 模糊理論 | zh_TW |
| dc.subject | 模糊回歸分析 | zh_TW |
| dc.subject | 局部加權 | zh_TW |
| dc.subject | Fuzzy theory | en_US |
| dc.subject | Fuzzy regression | en_US |
| dc.subject | Locally weighted method | en_US |
| dc.title | 模糊數據的局部加權回歸 | zh_TW |
| dc.title | Locally weighted regression of fuzzy data | en_US |
| dc.type | thesis | en_US |
| dc.relation.reference | [1] L.A. Zadeh, Fuzzy sets, Information and Control, Volume 8, Issue 3, June 1965, pp.338–353\n[2] H. Tanaka, S. Uejima, K. Asai，Linear regression analysis with fuzzy model， IEEE Trans. Sys., Man. Cyber., 12 (1982), pp. 903–907.\n[5] William S. Cleveland, Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association, Vol. 74,No. 368.(Dec., 1979),pp. 829-836.\n[6]Phil Diamond, Fuzzy Least Squares, Information Sciences 46(3), 1988, pp.141\n-157\n[7] Pierpaolo D`Urso, Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data ,Computational Statistics & Data Analysis, Volume 42, Issues 1–2, (2003), pp.47–72.\n[8] P. Anand Raj, D. Nagesh Kumar, Ranking alternatives with fuzzy weights using maximizing set and minimizing set ,Fuzzy Sets and Systems,1999,pp365-375\n[3]吳柏林，模糊統計導論第二版（2015），五南出版社（台北），p153.\n[4]陳孝煒、吳柏林，區間回歸與模糊樣本分析，管理科學與統計決策, 4(1), 2007 | zh_TW |
| item.grantfulltext | restricted | - |
| item.cerifentitytype | Publications | - |
| item.openairetype | thesis | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.fulltext | With Fulltext | - |
| Appears in Collections: | 學位論文 | |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 101801.pdf | 1.36 MB | Adobe PDF2 | View/Open |
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