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Information-theoretic Evaluation of Covariate Distributions Models by Hartung, Niklas; Khatova, Aleksandra is a scholarly article available to read on EtoBox.

What is Information-theoretic Evaluation of Covariate Distributions Models about?

Statistical modelling of covariate distributions allows to generate virtual populations or to impute missing values in a covariate dataset. Covariate distributions typically have non-Gaussian margins and show nonlinear correlation structures, which simple multivariate Gaussian distributions fail to represent. Prominent non-Gaussian frameworks for covariate distribution modelling are copula-based models and models based on multiple imputation by chained equations (MICE). While both frameworks have already found applications in the life sciences, a systematic investigation of their goodness-of-fit to the theoretical underlying distribution, indicating strengths and weaknesses under different conditions, is still lacking. To bridge this gap, we thoroughly evaluated covariate distribution models in terms of Kullback-Leibler divergence (KL-D), a scale-invariant information-theoretic goodness-of-fit criterion for distributions. Methodologically, we proposed a new approach to construct confidence intervals for KL-D by combining nearest neighbour-based KL-D estimators with subsampling-based uncertainty quantification. In relevant data sets of different sizes and dimensionalities with both

Author

Hartung, Niklas; Khatova, Aleksandra

Published

2024

Language

EN