Optimum shrinkage parameter selection for ridge type estimator of Tobit model

Abstract

This paper presents different ridge type estimators based on maximum likelihood ((Formula presented.)) for parameters of a Tobit model. In this context, an algorithm is introduced to get the estimators based on (Formula presented.). 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 ((Formula presented.)), Bayesian information criterion ((Formula presented.)), generalized cross-validation ((Formula presented.)), risk estimation using classical pilots ((Formula presented.)), Mallows’ ((Formula presented.)) and (Formula presented.) 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. © 2020 Informa UK Limited, trading as Taylor & Francis Group.