成對資料下名目與有序資料一致性kappa 參數的強韌概似分析;Robust likelihood analysis of the agreement kappa coefficient for paired nominal and paired ordinal data

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/77362

Title:	成對資料下名目與有序資料一致性kappa 參數的強韌概似分析;Robust likelihood analysis of the agreement kappa coefficient for paired nominal and paired ordinal data
Authors:	李怡萱;Lee, Yi-Hsuan
Contributors:	統計研究所
Keywords:	相關性資料;一致性Kappa;強韌概似函數;Correlated data;Agreement Kappa;Robust likelihood function
Date:	2018-06-29
Issue Date:	2018-08-31 14:35:25 (UTC+8)
Publisher:	國立中央大學
Abstract:	本文在診斷結果有三種可能的情況下，針對群集成對資料下的kappa 參數及加權 kappa 參數進行推論，建立kappa 參數之強韌概似函數。利用此強韌概似函數，在不需要特別對於群集間的相關性建立模型假設下，仍可以得到概似比檢定統計量及根據概似比檢定統計量所得到的信賴區間等正確的推論結果。同時，我們將用模擬與實例分析來比較我們的強韌推論方法與Yang and Zhou (2014, 2015)分別提出的在群集成對資料下的kappa 及加權kappa 的無母數推論方法。;In this paper, we construct a robust likelihood function for the agreement kappa/weighted kappa coefficient for clustered paired data in the case of three-category diagnostic outcome scenario. Utilizing this robust likelihood function, one can construct robust likelihood ratio (LR) statistic and LR-based confidence intervals without specifically modeling the intra-cluster correlation. We also make comparison between our robust likelihood approach and the nonparametric inferential method for kappa with paired data proposed by Yang and Zhou (2014, 2015) via simulations and real data analysis.
Appears in Collections:	[Graduate Institute of Statistics] Electronic Thesis & Dissertation

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