Statistical methods that shrink parameters towards zero can produce lower predictive variance than does maximum likelihood. This paper discusses an approach to doing this for age-period-cohort models, and applies it to fitting opioid mortality rates with a generalization of the Lee-Carter model...

Background: Bayesian regularization can address over-parameterization of age-period-cohort (APC) mortality models, facilitated by a new methodology for comparing fits of Bayesian regularized models. Here Bayesian Lasso is used to shrink slope changes in linear spline fits of the parameters of...