Penalized generalized least squares for model selection under restricted randomization Universiteit Antwerpen
For model selection purposes in experimental contexts, researchers often use step- wise regression or subset selection. With currently available software, this has to be done manually and often involves numerous model estimations in situations involv- ing restricted randomization, such as block experiments and split-plot experiments. Moreover, these selection procedures ignore the stochastic errors inherited in the variable selection stage. This ...