Convergence-Aware Pareto Selection of Covariate Scaling Transformations for Markov Deterioration Hazard Models: Evidence from Bridge Inspection Data

Takato Yasuno, Keita Kobayashi, Ryuta Sakaguchi

Maximum likelihood estimation of Markov deterioration hazard models for infrastructure asset management is sensitive to the numerical conditioning of explanatory covariates, yet production pipelines often adopt a single scaling convention without systematic justification. We study four covariate scaling transformations---baseline max scaling, min-max scaling, z-score scaling, and Box-Cox transformation with a training-derived positivity shift---applied to an Exponential Hazard Markov (EHM) model estimated via L-BFGS-B on bridge inspection transition data.