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Simplified Smoothing Splines for APC Models

Venter, Gary

Smoothing splines are splines fit including a roughness penalty. They can be used across groups of variables in regression models to produce more parsimonious models with improved accuracy. For APC (age-period-cohort) models, the variables in each direction can be numbered sequentially 1:N, which simplifies spline fitting. Further simplification is proposed using a different roughness penalty. Some key calculations then become closed-form, and numeric optimization for the degree of smoothing is simpler. Further, this allows the entire estimation to be done simply in MCMC for Bayesian and random-effects models, improving the estimation of the smoothing parameter and providing distributions of the parameters (or random effects) and the
selection of the spline knots.

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Academic Units
School of Professional Studies
Published Here
June 1, 2021