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Journal ArticleDOI

Modelling and generating correlated binary variables

Samuel D. Oman, +1 more
- 01 Feb 2001 - 
- Vol. 88, Iss: 1, pp 287-290
TLDR
This work presents an alternative class of correlation models which reflect the binary nature of the responses and allow for simple simulation of observations from these models.
Abstract
SUMMARY Many applications use simple parametric models for the correlation structure of binary responses which are observed in clusters. The usual approach, to use correlation models appropriate for normally distributed responses, suffers from two drawbacks when the marginal probabilities within the clusters differ. First, as it does not explicitly take into account constraints on the second moments which must be satisfied for binary responses, it may fail to model realistically the range of correlations present in the data. Secondly, computer simulation of observations from these models is very difficult. We present an alternative class of correlation models which reflect the binary nature of the responses and allow for simple simulation. Some key wor-ds: Binary variable; Computer simulation; Correlation structure; Generalised estimating equation.

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Citations
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Journal ArticleDOI

A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means and correlations

TL;DR: This work introduces a family of multivariate binary distributions with certain conditional linear property that is particularly useful for efficient and easy simulation of correlated binary variables with a given marginal mean vector and correlation matrix.
Journal ArticleDOI

Spatial-temporal rainfall simulation using generalized linear models

TL;DR: In this article, the problem of simulating sequences of daily rainfall at a network of sites in such a way as to reproduce a variety of properties realistically over a range of spatial scales is considered.
Journal ArticleDOI

Sequential Monte Carlo on large binary sampling spaces

TL;DR: In this paper, the authors present a parametric family for adaptive sampling on high dimensional binary spaces, which takes correlations into account, analogously to the multivariate normal distribution on continuous spaces.
Journal ArticleDOI

Range of correlation matrices for dependent Bernoulli random variables

TL;DR: In this article, necessary and sufficient conditions for compatibility for structured and unstructured correlation matrices are studied. But they do not consider the non-parametric binary models of Emrich & Piedmonte (1991) and Qaqish (2003) which allow a good range of correlations between binary variables.
Journal ArticleDOI

Imputation strategies for missing continuous outcomes in cluster randomized trials.

TL;DR: It is shown that cluster mean imputation yields valid inferences and given its simplicity, may be an attractive option in some large community intervention trials which are subject to individual-level attrition only; however, it may yield less powerful inferences than alternative procedures which pool across clusters especially when the cluster sizes are small and cluster follow-up rates are highly variable.
References
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Journal ArticleDOI

Longitudinal data analysis using generalized linear models

TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Journal ArticleDOI

Longitudinal data analysis for discrete and continuous outcomes.

Scott L. Zeger, +1 more
- 01 Mar 1986 - 
TL;DR: A class of generalized estimating equations (GEEs) for the regression parameters is proposed, extensions of those used in quasi-likelihood methods which have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as the authors often expect.
Journal ArticleDOI

Correlated binary regression with covariates specific to each binary observation.

TL;DR: It is argued that binary response models that condition on some or all binary responses in a given "block" are useful for studying certain types of dependencies, but not for the estimation of marginal response probabilities or pairwise correlations.
Journal ArticleDOI

Modelling multivariate binary data with alternating logistic regressions

TL;DR: This article proposed an alternative approach, alternating logistic regressions, for simultaneously regressing the response on explanatory variables as well as modelling the association among responses in terms of pairwise odds ratios.