Year: 2026
Type: preprint
Abstract: Generating data with desired properties for a logistic regression model under controlled conditions is challenging. We propose an integrated iterative bisection algorithm that determines values of the intercept and regression coefficients capable of generating data with a specified prevalence and predictive accuracy. The performance of the proposed method is demonstrated through simulation studies. We further show that the choice of parameter values, or, in a Bayesian setting, the choice of priors for these parameters, induces a particular level of predictive accuracy. This relationship should therefore be taken into account when generating data for model evaluation under controlled conditions.
Source: arXiv (Cornell University)
Authors: Abubakari, Kabiru, Liverani, Silvia
Institutions: Queen Mary University of London, Royal Botanic Gardens, Kew
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221132421