Joint Generation of Binary, Ordinal, Count, and Normal Data with Specified Marginal and Association Structures in Monte-Carlo Simulations-Reference-Cited by-同舟云学术

Joint Generation of Binary, Ordinal, Count, and Normal Data with Specified Marginal and Association Structures in Monte-Carlo Simulations

Published:2017 Issue: Volume: Page:3-15
ISSN:2199-0980
Container-title:Monte-Carlo Simulation-Based Statistical Modeling
language:
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Author:

Demirtas Hakan,Allozi Rawan,Hu Yiran,Inan Gul,Ozbek Levent

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-3307-0_1

Reference45 articles.

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2. Barbiero, A., & Ferrari, P. A. (2015). Simulation of ordinal and discrete variables with given correlation matrix and marginal distributions. R package GenOrd. https://cran.r-project.org/web/packages/GenOrd

3. Bates D., & Maechler M. (2016). Sparse and dense matrix classes and methods. R package Matrix. http://www.cran.r-project.org/web/packages/Matrix

4. Demirtas, H. (2004a). Simulation-driven inferences for multiply imputed longitudinal datasets. Statistica Neerlandica, 58, 466–482.

5. Demirtas, H. (2004b). Assessment of relative improvement due to weights within generalized estimating equations framework for incomplete clinical trials data. Journal of Biopharmaceutical Statistics, 14, 1085–1098.

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