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

Abstract: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.

Methodology (stat.ME); Machine Learning (cs.LG)

arXiv:2609.31786 [stat.ME]

(or arXiv:2609.31786v1 [stat.ME] for this version)

https://doi.org/10.48550/arXiv.2609.31786 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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