What’s New in SAS/STAT 14.1

New Procedures

GAMPL Procedure

The GAMPL procedure is a high-performance procedure that fits generalized additive models by penalized likelihood estimation. Based on low-rank regression splines (Wood 2006), these models are powerful tools for nonparametric regression and smoothing. Generalized additive models are extensions of generalized linear models. They relax the linearity assumption in generalized linear models by allowing spline terms in order to characterize nonlinear dependency structures. Each spline term is constructed by the thin-plate regression spline technique (Wood 2003). A roughness penalty is applied to each spline term by a smoothing parameter that controls the balance between goodness of fit and the roughness of the spline curve. PROC GAMPL fits models for standard distributions in the exponential family, such as normal, Poisson, and gamma distributions.

Both the GAMPL procedure and the GAM procedure in SAS/STAT software fit generalized additive models. However, the GAMPL procedure uses different approaches for constructing spline basis expansions, fitting generalized additive models, and testing smoothing components. It focuses on automatic smoothing parameter selection by using global model-evaluation criteria to find optimal models. The GAM procedure focuses on constructing models by fitting partial residuals against each smoothing term. In general, you should not expect similar results from the two procedures.


The SURVEYIMPUTE procedure imputes missing values of an item in a sample survey by replacing them with observed values from the same item. Imputation methods include single and multiple hot-deck imputation and fully efficient fractional imputation (FEFI) (Fuller 2009, section 5.2). Donor selection techniques include simple random selection with or without replacement, probability proportional to weights selection (Rao and Shao 1992), and approximate Bayesian bootstrap selection (Rubin and Schenker 1986). When you use FEFI, PROC SURVEYIMPUTE produces replicate weights that appropriately account for the imputation. You can use these replicate weights in any survey analysis procedure to correctly estimate the variance of an estimator that uses the imputed data.