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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/48227


    Title: 一維及二維右設限存活資料的適合度檢定;Goodness-of-fit tests for univariate and bivariate right censored survival data
    Authors: 李念純;Nien-chun Li
    Contributors: 統計研究所
    Keywords: 適合度檢定;右設限;Kolmogorov;Cramer-von Mises;關聯結構函數;Chi-square;goodness-of-fit test;Chi-square;right-censored;Kolmogorov;Cramer-von Mises;copula function
    Date: 2011-07-01
    Issue Date: 2012-01-05 14:42:00 (UTC+8)
    Abstract: 分析資料的統計方法有兩類:一種是無母數方法,另一種是有母數方法。雖然資料用無母數方法分析可以不假設任何特定的母體分布,但正確的使用有母數方法可以獲取較多的資訊。為能正確使用有母數方法,就必須根據資料建立分布的適合度檢定。本文分別在完整或右設限的一維度資料之下修正Kolmogorov和Cramer-von Mises統計式,在成對資料之下推廣修正Kolmogorov和Chi-square統計式進行資料分布的適合度檢定,此處的一維度資料考慮配適廣義伽瑪分布,成對資料則針對關聯結構函數做適合度檢定。本文以模擬的方法研究所提出適合度檢定的型I誤差率及檢定力的表現,最後以實例說明所提出檢定方法之應用。 There are two kinds of statistical methods for analyzing data: one is the nonparametric analysis and the other is the parametric analysis. We do not need to assume any particular form for the population distribution when we use a nonparametric method, however, correctly using a parametric method would produce more information on data analysis. To do so, we need to test the goodness-of-fit of a particular distribution based on the available data. In this paper, we construct goodness-of-fit tests for univariate and bivariate observations, respectively, with completely observed or right-censored data. Modifications of the Kolmogorov and Cramer-von Mises tests are proposed for testing the goodness-of-fit of the generalized gamma distribution for univariate data. Extensions of the Kolmogorov and Chi-square tests to testing the goodness-of-fit of a Copula function for bivariate data are then suggested. The results of a simulation study are presented for the investigation of type I error rates and powers of the proposed tests. Finally, the application of the tests is illustrated by using a real data set.
    Appears in Collections:[Graduate Institute of Statistics] Electronic Thesis & Dissertation

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