博碩士論文 101225022 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:44 、訪客IP:18.223.107.178
姓名 魏名君(MING-CHUN WEI)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 一個新的適合度檢定法
相關論文
★ 不需常態假設與不受離群值影響的選擇迴歸模型的方法★ 用卜瓦松與負二項分配建構非負連續隨機變數平均數之概似函數
★ 強韌變異數分析★ 用強韌概似函數分析具相關性之二分法資料
★ 利用Bartlett第二等式來估計有序資料的相關性★ 相關性連續與個數資料之強韌概似分析
★ 不偏估計函數之有效性比較★ 一個分析相關性資料的新方法-複合估計方程式
★ (一)加權概似函數之強韌性探討 (二)影響代謝症候群短期發生及消失的相關危險因子探討★ 利用 Bartlett 第二等式來推論模型假設錯誤下的變異數函數
★ (一)零過多的個數資料之變異數函數的強韌推論 (二)影響糖尿病、高血壓短期發生的相關危險因子探討★ 一個分析具相關性的連續與比例資料的簡單且強韌的方法
★ 時間數列模型之統計推論★ 複合概似函數有效性之探討
★ 決定分析相關性資料時統計檢定力與樣本數的普世強韌法★ 檢定DNA鹼基替換模型的新方法 - 考慮不同DNA鹼基間的相關性
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 統計分析的正確性取決於使用正確或合理的分配函數來配適資料,而適合度檢定是用來驗證使用的模型是否合適的方法。基於廣義線性模型的參數估計量是否有一致性,本論文提出一個新的統計量來進行適合度檢定,其中探討的重點在於資料是否來自伽瑪、韋伯或對數常態迴歸模型。我們使用模擬研究與實例分析來比較我們的方法與Kolmogorov-Smirnov Kolmogorov, 1933; Smirnov, 1939 、Cramér-von Mises Cramér, 1928; von Mises, 1931 與Anderson-Darling 1952 等三個使用經驗分配函數的適合度檢定統計量。
摘要(英) We propose new goodness fit of test approaches that are easy to implement with the bootstrapping techniques. The techniques are instituted by taking advantage of the fact that the mean regression parameters can be consistently estimated by using normal, gamma and Poisson models even when model fails. We test the appropriateness of the Weibull, log-normal and gamma model assumptions to illustrate the merit of our new methods.
We also compare our novel approaches with several commonly used and implemented existing methods with simulations and real data analyses.
關鍵字(中) ★ 適合度檢定
★ 伽瑪迴歸模型
★ 韋伯迴歸模型
★ 對數常態迴歸
關鍵字(英) ★ goodness of fit
★ gamma regression model
★ Weibull regression model
★ log-normal regression model
論文目次 摘要 i
Abstract i
誌謝辭 iii
目錄 iv
表目錄 vi
第一章 緒論 1
第二章 伽瑪、韋伯和對數常態迴歸模型的介紹 6
2.1 伽瑪迴歸模型 6
2.2 韋伯迴歸模型 10
2.3 對數常態迴歸模型 12
第三章 使用經驗分配函數的適合度檢定 15
3.1 三個檢定統計量 15
3.2 伽瑪、韋伯和對數常態迴歸模型的適合度檢定 17
第四章 適合度檢定的新方法 21
4.1 常態與卜瓦松迴歸模型 21
4.2 韋伯和對數常態迴歸模型的適合度檢定 24
4.3 特殊常態迴歸模型 28
4.4 伽瑪迴歸模型的適合度檢定 29
第五章 模擬研究 32
第六章 實例分析 65
第七章 結論 78
參考文獻 80
參考文獻 1. Anderson, T. W. and Darling, D. A. (1952). Asymptotic theory of certain goodness-of-fit criteria based on stochastic processes. Annals of Mathematical Statistics, 23, 193-212.
2. Bain, L. J. and Engelhardt, M. (1980). Probability of correct selection of Weibull versus gamma based on likelihood ratio. Communications in Statistics-Theory and Method, 9, 375-381.
3. Brynjarsdóttir, J. and Stefánsson, G. (2004). Analysis of cod catch data from Icelandic groundfish suveys using generalized linear model. Fisheries Research, 70, 195-208.
4. Cramér, H. (1928). On the composition of elementary errors, Scandinavian Actuarial Journal, 11, 13-74 and 141-180.
5. D’Agostino, R. B. and Stephens, M. A. (1986). Goodness-of-Fit Techniques. Marcel Dekker, New York.
6. Gupta, R. D., Kundu, D. and Manglick, A. (2002). Probability of correct selection of gamma versus GE or Weibull versus GE models based on likelihood ratio test. Recent Advances in Statistical Methods (Editor: Chaubey, Y. P.), Publisher World Scientific Publishing Company Inc., London, 147-156.
7. Hardin, J. W. and Hilbe, J. M. (2012). Generalized Linear Models and Extensions, Third Edition, Stata Press, Texas.
8. Kolmogorov, A. (1933). Sulla determinazione empirica di una legge di distribuzione, Giornalle dell′Instituto Italiano degli Attuari, 4, 83-91.
9. Kuiper, N. H. (1960). Tests concerning random points on a circle. Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, 63, 38-47
10. Krishnamoorthy, K. and Mathew, T. (2003). Inferences on the means of lognormal distributions using generalized p-values and generalized confidence intervals. Journal of Statistical Planning and Inference, 115, 103-121.
11. Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data, Second Edition, John Wiley, New York.
12. Lewis, P. A. W. (1961). Distribution of the Anderson-Darling statistic. Annals of Mathematical Statistics, 32, 1118-1124.
13. Myers, R. H., Montgomery, D. C., Vining, G. G., and Robinson, T. J. (2010). Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition, John Wiley, New York.

14. McCullagh, P. (1983). Quasi-likelihood functions. Annals of Statistics, 11, 59-67.

15. von Mises, R. (1931). Wahrscheinlichkeitsrechnung und ihre Anwendungen in der Statistik und der theoretischen Physik, Leipzig und Wien.
16. Nelder, J. and Wedderburn, R. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A, 135, 370-384.
17. Nelson, W. (1990). Accelerated testing: Statistical Models, Test Plants, and Data Analysis, John Wiley, New York.
18. Royall, R. M. and Tsou, T. S. (2003). Interpreting statistical evidence using imperfect models: robust adjusted likelihood functions. Journal of the Royal Statistical Society, Series B, 65, 391-404.
19. Smirnov, N. V. (1939). On the estimation of the discrepancy between empirical curves of distribution for two independent samples. Bulletin Mathematique de I′Universite de Moscou, 2, 3-14.
20. Stephens, M. A. (1974). EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69, 730-737.
21. Tsou, T. S. (2007). A simple and exploratory way to determine the mean-variance relationship in generalized linear models. Statistics in Medicine, 26, 1623-1631.
22. van der Vaart, A. W. (2000). Asymptotic Statistics, Cambridge University Press, Cambridge.
23. Watson, G. S. (1961). Goodness-of-fit tests on a circle. Biometrika. 48, 109-114
指導教授 鄒宗山 審核日期 2014-7-2
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明