Optimum shrinkage parameter selection for ridge type estimator of Tobit model

This paper presents different ridge type estimators based on maximum likelihood (ML) for parameters of a Tobit model. In this context, an algorithm is introduced to get the estimators based on ML. The most important issue in implementing these estimators is the selection of the optimum shrinkage parameter. Here attention is focused on the way in which the shrinkage parameter can be selected by six selection methods, including improved Akaike information criterion (AICc), Bayesian information criterion (BIC), generalized cross-validation (GCV), risk estimation using classical pilots (RECP), Mallows’ (Cp) and k^GM proposed by Kibria [Performance of some new ridge regression estimators. Commun Stat Simul Comput. 2003;32:419–435]. Monte Carlo simulation experiments are performed and a real data example is presented to illustrate the ideas in the paper. Hence, an appropriate selection criterion or criteria are provided for optimum shrinkage parameter.