2024
DOI: 10.1007/s11943-024-00345-1
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Automated Bayesian variable selection methods for binary regression models with missing covariate data

Michael Bergrab,
Christian Aßmann

Abstract: Data collection and the availability of large data sets has increased over the last decades. In both statistical and machine learning frameworks, two methodological issues typically arise when performing regression analysis on large data sets. First, variable selection is crucial in regression modeling, as it helps to identify an appropriate model with respect to the considered set of conditioning variables. Second, especially in the context of survey data, handling of missing values is important for estimatio… Show more

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