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姓名 李雪萍(Shueh-ping Lee) 查詢紙本館藏 畢業系所 統計研究所 論文名稱 相關性連續與個數資料之強韌概似分析
(Robust likelihood inference for correlated discrete and continuous data)相關論文 檔案 [Endnote RIS 格式]
[相關文章]
[文章引用]
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[館藏目錄]
至系統瀏覽論文 ( 永不開放)
摘要(中) 具相關性的連續與個數資料，如一個人看病的次數與其血壓值，常見於醫學或其它研究領域中。這種相關性的資料在分析上較困難，原因是不易找到合適的統計模型。
本文嘗試利用Royall與Tsou (2003)的強韌概似函數法，來建立一個對迴歸係數做不需知道正確模型的有母數強韌推論法。
摘要(英) This thesis is concerned with regression analysis of bivariate correlated count and continuous data. The mixed Poisson-normal is chosen as the working model for the joint distribution of the bivariate data.We then show that this working model can be corrected to become asymptotically robust against model misspecifications. Full likelihood inference about regression parameters is therefore made available without knowing the true underlying joint distributions.
Simulations and real data analysis are provided to demonstrate the efficacy of the new parametric robust likelihood approach for bivariate count-continuous data.
關鍵字(中) ★ 強韌概似函數
★ 卜瓦松迴歸模型
★ 混合模型關鍵字(英) ★ robust likelihood function
★ Poisson regression model
★ mixture model論文目次 中文摘要 ............................................................................................................... i
英文摘要 .............................................................................................................. ii
致謝辭 ................................................................................................................. iii
目錄 ..................................................................................................................... iv
表目錄 .................................................................................................................. v
第一章 緒論 ......................................................................................................... 1
第二章 強韌迴歸 ................................................................................................. 3
第三章 卜瓦松-常態混合模型之修正項 ............................................................ 6
3.1 卜瓦松-常態混合模型可被強韌化 ............................................................... 7
3.2 修正項之推導 ................................................................................................ 8
3.3 簡單迴歸架構下的修正項 .......................................................................... 14
第四章 模擬研究 ............................................................................................... 35
4.1 興趣參數 - 間斷型
參考文獻 Fitzmaurice, G.M. and Laird, N.M. (1997) “Regression models for mixed discrete and continuous responses with potentially missing values,”Biometrics, 53, 110-122.
Kang, J., Mao, K., Yang, Y., and Zhang, J. (2007) “Regression models for mixed Poisson and continuous longitudinal data,” Statistics in Medicine, 26, 3782-3800.
Little, R.J.A. and Schluchter, M.D. (1985) “Maximum likelihood estimation for mixed continuous and categorical data with missing values,”Biometrika, 72, 497-512.
Royall, R. and Tsou, T.S. (2003) “Interpreting statistical evidence by using imperfect models: robust adjusted likelihood functions,” J.R. Statist. Soc.B, 65, 391-404.
Tsou, T.S. (2006) “Robust Poisson regression,” Journal of Statistical Planning and Inference, 136, 3173-3186.
Zou, G. (2004) “A modified Poisson regression approach to prospective studies with binary data,” American Journal of Epidemiology, 159, 702-706.
指導教授 鄒宗山(Tsung-shan Tsou) 審核日期 2010-7-6 推文 plurk
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