| dc.contributor.advisor | 吳柏林<br>鄭宇庭 | zh_TW |
| dc.contributor.advisor | <br> | en_US |
| dc.contributor.author (Authors) | 張建瑋 | zh_TW |
| dc.contributor.author (Authors) | Chang ,Chien-Wei | en_US |
| dc.creator (作者) | 張建瑋 | zh_TW |
| dc.creator (作者) | Chang ,Chien-Wei | en_US |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 17-Sep-2009 18:47:26 (UTC+8) | - |
| dc.date.available | 17-Sep-2009 18:47:26 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Sep-2009 18:47:26 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0883545052 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/33911 | - |
| dc.description (描述) | 博士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 88354505 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 本文將模糊理論的觀念，應用在估計、檢定及時間數列分析上。研究重點包括離散型及連續型模糊樣本的定義與度量，模糊參數的最佳估計，模糊排序方法應用於無母數檢定，模糊相似度的定義、性質，以及如何將其應用於辨識不同時間數列間的落差l期相似程度等。我們首先將常見的模糊資料分為離散型及連續型，並針對不同類型的資料，給定對應的模糊平均數、模糊變異數等模糊參數的概念與一些重要性質。接著我們提出幾種估計方法，針對不同的模糊參數進行最佳估計並提出可行的評判準則。進一步地，我們將模糊排序方法應用於無母數檢定推論。最後我們提出模糊相似度的定義與度量。經由系統性的模擬與分析，我們建立兩時間數列間模糊相似度演算法則。實證分析方面，我們利用提出的方法對台灣的股價加權指數、個股股價進行估計及檢定；同時，針對台灣歷年GDP、民間消費、毛投資間的相似性進行偵測，以驗證我們提出的模糊參數估計、模糊無母數檢定及模糊相似度演算法的效率性與實用性。 | zh_TW |
| dc.description.abstract (摘要) | In this paper, we apply fuzzy theory in estimation, nonparametric test, and time series analysis. Our focus is on: How to define and measure the discrete type fuzzy data and continuous one? How to find the optimal estimators for fuzzy parameters? How to apply fuzzy ranking methods in nonparametric test when the data is vague? How to define and find the degree of fuzzy similarity between two time series? First, fuzzy data is classified according to its type, discrete or continuous. Then we give some definitions and properties on fuzzy mean, fuzzy variance for different type of fuzzy data. Next, we proposed some estimating methods and evaluation rules. Moreover we apply fuzzy ranking methods in nonparametric test, such as Sign test, Wilcoxon signed rank test, Wilcoxon rank sum test, and so on. Finally, we suggest the definitions as well as the algorithm for computing the degree of fuzzy similarity between two time series. We also give some simulate and empirical examples to illustrate the techniques and to analyze fuzzy data. Results show that fuzzy statistics with soft computing are more realistic and reasonable for the social science research. | en_US |
| dc.description.tableofcontents | 第1章 前言 11.1 模糊均數與變異數估計 11.2 模糊樣本排序及無母數檢定方法 21.3 模糊相似度估計 4第2章 模糊均數與變異數之估計 62.1 模糊均數與變異數 62.2 模糊母體均數最佳估計方法 162.3 實證分析應用 20第3章 模糊樣本排序及無母數檢定方法 233.1 模糊樣本之排序 233.2 模糊中位數於符號檢定(SIGN TEST)之應用 253.3 模糊樣本排序方法應用於威克生符號等級檢定 283.4 模糊樣本排序方法應用於威克生等級和檢定 303.5 模糊樣本排序方法應用於KRUSKAL-WALLIS 檢定 333.6 模糊類別資料之卡方 齊一性檢定 343.7 實證分析應用 37第4章 模糊統計相似度估計 404.1相似度 404.2相似性的探討 414.3 模糊時間數列 424.4 時間數列模糊相似性演算法則 464.5 模擬結果 484.6 實證分析 55第5章 結論與未來研究方向 61參考文獻 63 | zh_TW |
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| dc.format.mimetype | application/pdf | - |
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| 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/#G0883545052 | en_US |
| dc.subject (關鍵詞) | 模糊數 | zh_TW |
| dc.subject (關鍵詞) | 模糊區間 | zh_TW |
| dc.subject (關鍵詞) | 軟統計分析 | zh_TW |
| dc.subject (關鍵詞) | 模糊平均數 | zh_TW |
| dc.subject (關鍵詞) | 模糊變異數 | zh_TW |
| dc.subject (關鍵詞) | 估計 | zh_TW |
| dc.subject (關鍵詞) | 最佳估計 | zh_TW |
| dc.subject (關鍵詞) | 無母數檢定 | zh_TW |
| dc.subject (關鍵詞) | 時間數列 | zh_TW |
| dc.subject (關鍵詞) | 相似性 | zh_TW |
| dc.title (題名) | 模糊資料之軟統計分析及檢定 | zh_TW |
| dc.type (資料類型) | thesis | en |
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