| Summary: | 碩士 === 國立臺灣大學 === 流行病學研究所 === 94 === Cohen''s kappa is a popular index to measure the beyond-chance agreement. In this thesis, I propose a Bayesian approach to study the agreement for the case of two raters with binary ratings in the setting of reliability test. In other words, I focus on the kappa under the assumption of equal marginal probability of positive classification. Two kinds of Jeffreys'' priors are used in inference. One is a hierarchical prior based on efficient Fisher information, and the other is a joint prior based on Fisher information matrix. In general, the resulting two estimators of posterior mode of kappa are very similar. Simulation studies with small and moderate sample size are conducted to evaluate the performance of two Bayesian estimators and MLE. Results show that the posterior mode of kappa based on efficient Fisher information is the best among three estimators. In addition, it is recommended to use a non-informative prior for in most cases. Bayesian method can handle easily even some special data.
|
|---|
