Parameter Reduction in Actuarial Triangle Models

Very similar modeling is done for actuarial models in loss reserving and mortality projection. Both start with incomplete data rectangles, traditionally called triangles, and model the data by year of origin, year of observation, and lag from origin to observation. Actuaries using these models almost always use some form of parameter reduction because there are too many parameters to fit reliably, but usually such adjustment is an ad hoc exercise. In this paper, we try two formal statistical approaches to parameter reduction, random effects and LASSO (least absolute shrinkage and selection operator), and discuss methods of comparing goodness of fit.