Social and Behavioral Sciences Commons^™

Risk Properties Of A Stein-Like Estimator For Multinomial Choice Models, Vera Alexandrova Tabakova

LSU Doctoral Dissertations

Stein-rule estimators, also known as shrinkage estimators, combine sample and non-sample information in a way that improves the precision of the estimation process or the quality of subsequent predictions. A Stein-rule estimator is a weighted average of a restricted and an unrestricted estimator, where the weights determine the degree of shrinkage, i.e. the importance that we place on the non-sample information. The existing literature shows that Stein-rule estimators may lead to squared error risk improvements in the linear regression, and in a number of non-linear models. The dissertation explores Stein-rule estimation in the context of multinomial choice models. It consists …