The ADAPTIVEREG procedure fits multivariate adaptive regression splines. The method is a nonparametric regression technique that combines both regression splines and model selection methods. It constructs spline basis functions in an adaptive way by automatically selecting appropriate knot values for different variables. The procedure performs model reduction by applying model selection techniques. Thus, the ADAPTIVEREG procedure is both a nonparametric regression procedure and a predictive modeling procedure.
The ADAPTIVEREG procedure supports models with classification variables, and it provides options for improving modeling speed. PROC ADAPTIVEREG extends the method to data with response variables that are distributed in the exponential family, including the binomial, Poisson, negative binomial, gamma, and inverse Gaussian distributions. PROC ADAPTIVEREG is multithreaded, enabling it to take advantage of multiple processors.
The QUANTLIFE procedure implements two quantile regression approaches that have been developed to account for right-censoring and provide valid estimates. Portnoy (2003) proposed a method to estimate conditional quantile functions from survival data by generalizing the idea of the Kaplan-Meier estimator of the survival function, and Peng and Huang (2008) developed a quantile regression approach motivated by the Nelson-Aalen estimator of the cumulative hazard function. Both methods are implemented with linear programming algorithms in the QUANTLIFE procedure. Like the standard quantile regression method for uncensored data, these two methods are distribution-free and are applicable to heteroscedastic data.
The QUANTLIFE procedure produces survival plots, conditional quantile plots, and quantile process plots. It performs semiparametric quantile regression when you specify spline effects. PROC QUANTLIFE takes advantage of parallel computing when multiple processors are available.
The QUANTSELECT procedure performs model selection for linear quantile regression. It provides capabilities similar to those offered by the GLMSELECT procedure (provides model selection for univariate linear models) including forward, backward, stepwise, LASSO, and adaptive LASSO selection methods. You can specify criteria such as AIC, SBC, AICC, adjusted R1, and significance levels to compare the fit of models, to determine when to stop the model selection process, and to choose the final selection model. The QUANTSELECT procedure supports constructed effects (such as smoothing splines and polynomials) and the SPLIT option for its CLASS statement. PROC QUANTREG produces parameter estimates progression plots and a variety of criterion progression plots.
PROC QUANTSELECT supports constructed effects such as regression spline, and it enables you to partition your data into training, validation, and testing roles. It is also multithreaded so that it can take advantage of multiple processors.
PROC QUANTSELECT is very efficient and can handle hundreds of variables and thousands of observations.
The STDRATE procedure computes direct and indirect standardized rates and risks for study populations. With direct standardization, you compute the weighted average of stratum-specific estimates in the study population, using weights such as population-time from a standard or reference population. With indirect standardization, you compute the weighted average of stratum-specific estimates in the reference population by using weights from the study population. The procedure provides summary statistics such as rate and risk estimates (and their confidence limits) for each stratum, as well as graphs.