Kernel smoothing techniques have attracted much attention and some notoriety in recent years. The attention is well deserved as kernel methods free researchers from having to impose rigid parametric structure on their data. The notoriety arises from the fact that the amount of smoothing (i.e.,...

A class of kernel regression estimators is developed for a broad class of hierarchical models including the pooled regression estimator, the fixed-effect model familiar from panel data, etc. Separate shrinking is allowed for each coefficient. Regressors may be continuous or discrete. The...