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Generating Correlated Binary Variables with Complete Specification of the Joint Distribution
Author(s) -
Kang SeungHo,
Jung SinHo
Publication year - 2001
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/1521-4036(200106)43:3<263::aid-bimj263>3.0.co;2-5
Subject(s) - binary data , multinomial distribution , binary number , joint probability distribution , monte carlo method , mathematics , random variate , computer science , asymptotic analysis , statistics , random variable , arithmetic
Most statistical methods for the analysis of correlated binary data are based on asymptotic theory. Therefore it is important to generate correlated binary data efficiently for Monte Carlo simulation studies to investigate the finite sample performance of these methods. This article provides a simple method for generating correlated binary data with a given joint distribution. The key idea is to consider k ‐variate binary data as a multinomial distribution with 2 k possible outcomes.