Simulation results for evaluating feature selection and prediction performance based on 400 Monte Carlo data sets, where n = 125 and d = 8. Lasso-PL, Lasso partly linear AFT model; Lasso-SK, Lasso stratified model with K strata; Lasso-L, Lasso linear AFT model assuming a linear effect for both Xi and Zi; Lasso-Cox, Lasso linear Cox model assuming a linear effect for both Xi and Zi; AFT, standard AFT model assuming linear effects for both Xi and Zi without regularization; and Oracle, oracle partly linear model with zero coefficients being set to 0. Δ, effect size; SSE, sum of squared errors for ϑ̂; PC, proportion of zero coefficients being set to zero; PI, proportion of nonzero coefficients being set to zero; MSPE1, squared prediction error using both nonlinear and linear components; and MSPE2, squared prediction error using only linear components. Range of SEs, range of SEs for the corresponding performance measure in each column. NA, a performance measure is not applicable for an estimator. All numbers are multiplied by 1000.