  This paper describes a method to estimate a production frontier that
satisfies the axioms of monotonicity and concavity in a non-parametric Bayesian
setting. An inefficiency term that allows for significant departure from prior
distributional assumptions is jointly estimated in a single stage with
parametric prior assumptions. We introduce heteroscedasticity into the
inefficiency terms by local hyperplane-specific shrinkage hyperparameters and
impose monotonicity using bound-constrained local nonlinear regression. Our
minimum-of-hyperplanes estimator imposes concavity. Our Monte Carlo simulation
experiments demonstrate that the frontier and efficiency estimations are
competitive, economically sound, and allow for the analysis of larger datasets
than existing nonparametric methods. We validate the proposed method using data
from 2007-2010 for Japan's concrete industry. The results show that the
efficiency levels remain relatively high over the time period.
