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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

多變量模擬輸出之統計分析

許淑卿, XU, SHU-GING Unknown Date (has links)
本論文共一冊,分八章八節。 內容:本論文所擬探討之對象為多變量統計分配函數模擬(Simulation)之最佳停止 法則問題(Optimal Stopping Rule Problem ),此類問題之目的在於如何利用盡量 小的樣本數之觀察值來求得未知母數(Unknoron Parameter)的信區間(域)(Co- nfidence interval )(Confidence Region),而此信賴區間(域)之寬度(Width )及包含機率(Coverage Probability)均已事先指定。 以往研究對象多傴限於單變量統計分配函數,而多變量統計分配函數模擬之最佳停止 法則問題,仍尚在研究階段,因此本論文之重點乃在於探討如何求得滿足最佳停止法 則之最小樣本數。在此以多變量常態分配函數為重心,並進而嗜試推廣至其他多數量 統計分配函數。
2

分配函數關聯性之研究

吳江名, Wu, Jiang-Ming Unknown Date (has links)
本文主要討論各種分配函數之間所存在的關聯性。分為六章摘要如下: 第一章: 緒論, 介紹本文研究之動機、目的、方法以及所遭遇之研究限制等事項。 第二章: 由皮爾生分配體系之觀點, 討論各種分配函數間的關係, 共分二節。 第三章: 討論各種離散分配間之關係, 共分六節。 第四章: 討論各種連續分配間之關係, 共分九節。 第五章: 討論各種抽樣分配間之關係, 共分七節。 第六章: 結論, 對前四章之結果作一總結, 並略述其應用。
3

模糊隨機變數在線性迴歸模式上的應用 / Fuzzy Random Variables and Its Applications in Fuzzy Regression Model

曾能芳 Unknown Date (has links)
傳統迴歸分析是假設觀測值的不確定性來自於隨機現象,本文則應用模糊隨機變數概念於迴歸模式的架構,考慮將隨機現象和模糊認知並列研究。針對樣本模糊數(x<sub>i</sub>, Y<sub>i</sub>),我們進行模糊迴歸參數估計,並稱此為模糊迴歸模式分析。模糊迴歸參數估計大都採用線性規劃,求出適當區間,將觀測模糊數Y<sub>i</sub>的分佈範圍全部覆蓋。但是此結果並不能充分反映觀測樣本Y<sub>i</sub>的特性。本研究提出一套模糊迴歸參數的估計方法,其結果對觀測樣本的解釋將更為合理,且具有模糊不偏的特性。在分析過程中,我們亦提出一些模糊統計量如模糊期望值、模糊變異數、模糊中位數的定義,以增加對這些參數的模糊理解。最後在本文中也針對台灣景氣指標與經濟成長率作實務分析,說明模糊迴歸模式的適用性。 / Conventional study on the regression analysis is based on the conception that the uncertainty of observed data comes from the random property. However, in this paper we consider both of the random property and the fuzzy perception to construct the regression model by using of fuzzy random variables. For the fuzzy sample (x<sub>i</sub>,Y<sub>i</sub>), we will process the parameters estimation of the fuzzy regression, and we call this process as fuzzy regression analysis. The parameters estimation for a fuzzy regression model is generally derived by the linear programming scheme. But it's result usually doesn't sufficiently reflect the characteristics of the observed samples. Hence in this paper we propose an alternative technique for parameters estimation in constructing the fuzzy regression model. The result will describe the observed data better than the conventional method did, moreover it will have the fuzzy unbiased properties. For the purpose of fuzzy perception on the fuzzy random variables, we also give definitions for certain important fuzzy statistics such as fuzzy expected value, fuzzy variance and fuzzy median. Finally, we give an example about the Taiwan Business Cycle and the Taiwan Economic Growth Rate for illustration.

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