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On Synthetic Interval Data with Predetermined Subject Partitioning and Partial Control of the Variables’ Marginal Correlation Structure

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  • Michail Papathomas

    (School of Mathematics and Statistics, University of St. Andrews, St. Andrews KY16 9AJ, UK
    Current address: The Observatory, Buchanan Gardens, St. Andrews KY16 9LZ, UK.)

Abstract

A standard approach for assessing the performance of partition models is to create synthetic datasets with a prespecified clustering structure and assess how well the model reveals this structure. A common format involves subjects being assigned to different clusters, with observations simulated so that subjects within the same cluster have similar profiles, allowing for some variability. In this manuscript, we consider observations from interval variables. Interval data are commonly observed in cohort and Genome-Wide Association studies, and our focus is on Single-Nucleotide Polymorphisms. Theoretical and empirical results are utilized to explore the dependence structure between the variables in relation to the clustering structure for the subjects. A novel algorithm is proposed that allows control over the marginal stratified correlation structure of the variables, specifying exact correlation values within groups of variables. Practical examples are shown, and a synthetic dataset is compared to a real one, to demonstrate similarities and differences.

Suggested Citation

  • Michail Papathomas, 2025. "On Synthetic Interval Data with Predetermined Subject Partitioning and Partial Control of the Variables’ Marginal Correlation Structure," Stats, MDPI, vol. 8(3), pages 1-18, August.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:3:p:78-:d:1734074
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    References listed on IDEAS

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    1. Dunson, David B. & Xing, Chuanhua, 2009. "Nonparametric Bayes Modeling of Multivariate Categorical Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1042-1051.
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