分析交叉設計得到的連續資料的強韌概似法;Robust likelihood inference for crossover designs for continuous data

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/89640

Title:	分析交叉設計得到的連續資料的強韌概似法;Robust likelihood inference for crossover designs for continuous data
Authors:	邱奕慶;Chiu, Yi-Ching
Contributors:	統計研究所
Keywords:	交叉設計;強韌概似函數;強韌分數檢定;魏克生符號檢定;Crossover design;Robust likelihood function;Robust score test;Wilcoxon signed rank
Date:	2022-09-14
Issue Date:	2022-10-04 11:50:34 (UTC+8)
Publisher:	國立中央大學
Abstract:	在臨床試驗中，實驗者常用的試驗手法為交叉設計 (crossoverdesign)。本文使用獨立常態模型與獨立伽瑪模型作為實作模型 (working model)，並利用強韌概似函數 (robust likelihood function) 方法，使得實作模型強韌化。進一步得到強韌華德檢定統計量 (robust Wald statistics)、強韌分數檢定統計量 (robust score statistics)、強韌概似比檢定統計量 (robust likelihood ratio statistics)，也可得到各個檢定的信賴區間。藉由模擬研究與實例分析，比較強韌華德檢定統計量、強韌分數檢定統計量、強韌概似比檢定統計量、Seen (1994) 提出的 CROS 檢定統計量和 Putt and Chinchilli (2004) 提出的魏克生符號檢定 (Wilcoxon signed rank)。不論實作模型與資料間的假設是否相同，透過強韌概似函數方法可以得出正確的統計推論。;In clinical trials, the experimental technique commonly used by experimenters is a crossover design. In this paper, independent normal model and independent gamma model are used as working model, and the robust likelihood function method is used to robust working model. Further obtain the robust Wald test statistics , the robust score test statistics , the robust likelihood ratio test statistics , and confidence intervals for individual tests are also available. By using simulation research and real data analysis, we can compare the robust Wald test statistic, robust score test statistic, robust likelihood ratio test statistic, CROS test statistic proposed by Seen (1994), and Putt and Chinchilli (2004) proposed Wilcoxon signed ranked. Regardless of whether the assumption between the working model and the data are the same, robust likelihood function method can obtain correctly statistical inferences.
Appears in Collections:	[Graduate Institute of Statistics] Electronic Thesis & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	154	View/Open

社群 sharing

Loading...