An adaptive estimator is proposed to optimally estimate unknown truncation points of the error support space for the general linear model. The adaptive estimator is specified analytically to minimize a risk function based on the squared error loss measure. It is then empirically applied to a...

In this paper we illustrate the use of alternative truncated regression estimators for the general linear model. These include variations of maximum likelihood, Bayesian, and maximum entropy estimators in which the error distributions are doubly truncated. To evaluate the performance of the...

This study examines the implications of the short-run specification of the standard, static translog cost function along with the possible implications of non-stationarity by estimating a dynamic translog cost specification complete with dynamic share equations for the U.S. using an empirical...

Second degree stochastic dominance (SSD) can be, but seldom is explicitly, applied to problems having continuous variables. A model is presented which, for any SSD efficient solution, facilitates exploration of the set of SSD consistent shadow prices. The model is tested by applying it to a...

Training a neural network is a difficult optimization problem because of numerous local minimums. Many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a...