I will consider estimation and prediction problems in generalized linear models when there are a number of predictors and some of them may have no and/or weak impact in predicting the response variable. In the context of two competing models where one model includes all predictors and the other restricts variable coefficients to a candidate linear subspace based on subject matter or prior knowledge, we investigate the relative performances of Stein type shrinkage, pretest, and penalty estimators with respect to the full model estimator. The asymptotic properties of the non-penalty estimators are documented. A Monte Carlo simulation study show that the mean squared error (MSE) of an adaptive shrinkage estimator is comparable to the risk of the penalty estimators in many situations and in particular performs better than the penalty estimators when the dimension of the restricted parameter space is large model. A real data set analysis is also offered to compare the relative performance of suggested strategies.