A Bayesian Approach for Estimating Twin Similarity with Respect to a Binary Trait Under the Beta-Binomial Model
(2011). A Bayesian Approach for Estimating Twin Similarity with Respect to a Binary Trait Under the Beta-Binomial Model. EKB Journal Management System, 55(1), 10-21. doi: 10.21608/esju.2011.314308
. "A Bayesian Approach for Estimating Twin Similarity with Respect to a Binary Trait Under the Beta-Binomial Model". EKB Journal Management System, 55, 1, 2011, 10-21. doi: 10.21608/esju.2011.314308
(2011). 'A Bayesian Approach for Estimating Twin Similarity with Respect to a Binary Trait Under the Beta-Binomial Model', EKB Journal Management System, 55(1), pp. 10-21. doi: 10.21608/esju.2011.314308
A Bayesian Approach for Estimating Twin Similarity with Respect to a Binary Trait Under the Beta-Binomial Model. EKB Journal Management System, 2011; 55(1): 10-21. doi: 10.21608/esju.2011.314308

A Bayesian Approach for Estimating Twin Similarity with Respect to a Binary Trait Under the Beta-Binomial Model

The Egyptian Statistical Journal
Article 2, Volume 55, Issue 1, June 2011, Page 10-21  XML
Document Type: Original Article
DOI: 10.21608/esju.2011.314308
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Abstract
In twin studies there is a need to compare a sample of monozygotic to another independent sample of dizygotic twins with respect to the presence or absence of a specific disease or condition. A significant difference between the intracluster correlation coefficients between the two independent samples indicates the presence of a genetic component in the disease etiology, and is an estimate of twin similarity. In this paper, Bayesian statistical methodology is presented for the estimation of twin similarity with respect to a dichotomous trait. The parameter of interest is the difference between the intracluster correlation coefficients (ICC) of the two zygosity groups. Under the Beta- Binomial model, point and interval estimators for this difference, 4, are obtained.
 
Keywords
Beta; Binomial Model; Interval Estimation; Intracluster Correlation
Statistics
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