The likelihood function for the bivariate survivor function F, under independent censorship, is maximized to obtain a non-parametric maximum likelihood estimator F K . F K may or may not be unique depending on the con"guration of singly-and doubly-censored pairs. The likelihood function can be maxim
MAXIMUM LIKELIHOOD ESTIMATION OF THE KAPPA COEFFICIENT FROM BIVARIATE LOGISTIC REGRESSION
โ Scribed by M. M. SHOUKRI; I. U. H. MIAN
- Publisher
- John Wiley and Sons
- Year
- 1996
- Tongue
- English
- Weight
- 621 KB
- Volume
- 15
- Category
- Article
- ISSN
- 0277-6715
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โฆ Synopsis
We propose a maximum likelihood estimator (MLE) of the kappa coefficient from a 2 x 2 table when the binary ratings depend on patient and/or clinician effects. We achieve this by expressing the logit of the probability of positive rating as a linear function of the subject-specific and the rater-specific covariates. We investigate the bias and variance of the MLE in small and moderate size samples through Monte Carlo simulation and we provide the sample size calculation to detect departure from the null hypothesis Ho: kappa = K~ in the direction of HI: kappa > K ~.
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