A Method for Generating High-Dimensional Multivariate Binary Variates

Emrich, Lawrence J.; Piedmonte, Marion

doi:10.1080/00031305.1991.10475828

Cited by 209 publications

(139 citation statements)

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“…Emrich and Piedmonte (1991) [14] and Demirtas and Doganay (2012) [9] discuss multivariate generation of binary and mixed data from multivariate normally distributed values, respectively. In introducing these methods, we first present some pairwise correlation coefficients, such as the phi, tetrachoric, and point-biserial correlations.…”

Section: Binary and Mixed Data Generationmentioning

confidence: 99%

“…Demirtas and Doganay (2012) [9] extend the method of Emrich and Piedmonte (1991) [14] to generate multivate mixed data. They use principles from Emrich and Piedmonte (1991) [14] to generate binary variables.…”

Section: Binary and Mixed Data Generationmentioning

confidence: 99%

“…Lastly, z(p j ) and z(p k ) are the quantile functions of the standard normal distribution for p j and p k , respectively. Emrich and Piedmonte (1991) [14] indicate that the multivariate normally distributed data generated using a correlation matrix with the pairwise tetrachoric correlations can then be dichotomized by quantiles based on proportions corresponding to the desired binary data. The third correlation we discuss here, the point-biserial correlation, assesses the relationship between a binary variable and a continuous variable.…”

Section: Binary and Mixed Data Generationmentioning

confidence: 99%

“…and Goldberg (1998) [18] algorithm is discussed in Section 3, eCDF computations are discussed in Section 4, and methods presented in Emrich and Piedmonte (1991) [14] and Demirtas and Doganay (2012) [9] are discussed in Section 5. Our new method is proposed in Section 6 and applications of the method to simulated data and real data examples are included in Sections 7 and 8, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The method given in Barton and Schruben (1993) [1] is involved in back-transformation of eCDF values to the scale of the original data at the final step for imputation of continuous variables. For binary variables, transformation and back-transformations of the data are associated with principles from Emrich and Piedmonte (1991) [14] and Demirtas and Doganay (2012) [9] for binary and mixed data generation, respectively. The backround involving the Lurie * Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

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A semi-parametric approach for imputing mixed data

Helenowski

Demirtaş

2013

Statistics and Its Interface

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In this work, we present a semi-parametric method for imputing mixed data which allows us to relax assumptions of the general location model. This approach involves transforming continuous and binary variables to normally distributed data, imputing the data via joint modeling under the normality assumption, and back-transforming the data to their original scale. Transformation and backtransformation of the data comprise the nonparametric portion, and multiple imputation under the normality assumption constitutes the parametric portion of our method. Simulations involving generated mixed data with binary variables and with continuous variables following normal, t, Gamma, and mixture Gamma distributions and real data applications indicate promising results, leading us to recommend our approach as a possible avenue for imputing mixed data by semi-parametric means.

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Section: Binary and Mixed Data Generationmentioning

confidence: 99%

Section: Binary and Mixed Data Generationmentioning

confidence: 99%

Section: Binary and Mixed Data Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A semi-parametric approach for imputing mixed data

Helenowski

Demirtaş

2013

Statistics and Its Interface

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Interval estimation of the common odds ratio fromk(2×2) tables under cluster sampling

Begg

Panageas

1999

Statist. Med.

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We propose a simple correction factor for the variance of the logarithm of the common odds ratio estimated by the method of Mantel and Haenszel from a series of (2 x 2) tables when data are cluster correlated. The adjustment is applied to the variance estimators proposed by Hauck and by Robins, Breslow and Greenland for the log of the Mantel-Haenszel common odds ratio, and its performance is evaluated in a simulation study. The key features of the proposed adjustment are: (i) it has closed-form; (ii) it can accommodate covariates defined at the cluster-specific level, the site-specific level, or both; and (iii) it does not require the user to specify a particular correlation structure for the response data. The correction derives from Liang and Zeger's generalized estimating equations (GEE) technique for logistic regression modelling. Via simulation, we examine empirical versus nominal coverage probabilities for interval estimation of the common odds ratio using adjusted and unadjusted variance estimates, and we present ratios of observed to estimated variances. Results are compared to those obtained from the fully iterated GEE analysis. The characteristics of the simulation study mimic scenarios common in the periodontal research setting, with small numbers of subjects (N = 25, 50), moderate numbers of sites per cluster (m = 4, 16, 32), and modest intracluster correlation levels (rho = 0.0, 0.1, 0.2, 0.3). Results show that adjusted confidence intervals (applied to the Hauck or the Robins, Breslow, Greenland variance estimate) provide coverage probabilities close to the nominal level for the Mantel--Haenszel common odds ratio over a variety of cluster sizes and levels of correlation.

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Generating Correlated Binary Variables with Complete Specification of the Joint Distribution

Kang

Jung

2001

Biom. J.

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A Method for Generating High-Dimensional Multivariate Binary Variates

Cited by 209 publications

References 4 publications

A semi-parametric approach for imputing mixed data

A semi-parametric approach for imputing mixed data

Interval estimation of the common odds ratio fromk(2×2) tables under cluster sampling

Generating Correlated Binary Variables with Complete Specification of the Joint Distribution

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