Generating correlated binary variables with complete specification of the joint distribution

Seung Ho Kang, Sin Ho Jung

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

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 2k possible outcomes.

Original languageEnglish
Pages (from-to)263-269
Number of pages7
JournalBiometrical Journal
Volume43
Issue number3
DOIs
Publication statusPublished - 2001

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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