April 8 – Georgia Tech – Large Scale Benchmarking of Manufacturing Performance


Establishment level manufacturing data are gathered from several countries around the world. These datasets are gathered by the Census Bureau of the respective countries and is an exhaustive Census of all establishments in some years. For example the U.S. Census Bureau performs a full Census of all manufacturing establishments in years ending in 2 and 7 and performs a survey sampling only 15 percent of establishments in other years.

This talk will be structured around three topics regarding census manufacturing datasets. First we will describe model development, estimation, and data challenges related to analyzing these datasets. Second, we propose a framework using the actual dataset to determine the best functional estimation method in contrast to the standard framework of Monte Carlo simulation. The results from our framework provide insights regarding the appropriate survey sizes needed in non-census years. Third, we will describe results from analyzing Japanese and Chilean data and will discuss future questions to investigate for these and other countries.

December 2 – Workshop 2015 -Advances in DEA Theory and Applications – Predictive Efficiency Analysis: A Study of U.S. Hospitals


Healthcare costs are higher in the U.S. then anywhere else in the world. A significant portion of the costs are generated in hospitals. We investigate both the efficiency and the effectiveness of U.S. community hospitals using the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project 2009-2011 Nationwide Inpatient Sample, a data set which contains all discharges from an approximate 20% sample of hospitals.

Here efficiency is the productivity of the hospital measured relative to the most productive hospitals and effectiveness is how closely the hospital produced relative to the forecasted services needed. We find the effectiveness levels are slightly higher than the efficiency levels in both 2010 and 2011 indicating that hospitals are producing closer to the forecasted level than the actual service level needed. Further, both efficiency and effectiveness levels are low indicating a large variability in the level of resources hospitals use to provide the same set of services. The low effectiveness scores indicate that many hospitals have a high level of resources even relative to the forecasted demand providing some evidence for a medical arms race.

November 3 – Informs Annual Meeting – Shape Constrained Kernel Weighted Least Squares for the Estimation of Production Functions


This paper proposes a unifying model and estimator we call Shape Constrained Kernel-weighted Least Squares (SCKLS). We show the relationship between the SCKLS estimator and both the Convex Nonparametric Least Squares (CNLS) and Du’s estimators. Specifically, the SCKLS estimator converges to the CNLS estimator as the bandwidth goes to zero. We compare the performance of the three estimators (SCKLS, CNLS, and Du’s estimator) via Monte Carlo simulations.

September 24 – Penn State University: Production Economics for Performance Evaluation of Engineered Systems: The Example of Finnish Electricity Distribution

Nimetön 7

The Finnish electricity market has a competitive energy generation market and a monopolistic transmission system. To regulate the local monopoly power of network operators, the government regulator uses frontier estimation methods. We describe the new regulatory system developed for the Finnish regulator, which is based on the method Stochastic semi-Nonparametric Envelopment of Data (StoNED) and utilizes panel data to detect the excessive costs from random noise.

EWEPA: Shape Constrained Nonparametric Estimation of Production Functions


This presentation was given at the European Workshop for Efficiency and Productivity Analysis (EWEPA XIV) as the plenary talk, June 16, 2015 in Helsinki Finland. I focus on the benefits of shape constraints to improve the finite sample performance of nonparametric estimators. Existing estimators and new estimators are presented that combine kernel smoothing techniques with axiomatic functional estimation.



November 9th – Informs Annual Conference: A Multivariate Seminonparametric Bayesian Concave Regression Method to Estimate Stochastic Frontiers


This presentation discusses a method that incorporates the latest advances in the Bayesian constrained regression literature offering an alternative to the current Least Squares-based and Kernel Regression-based Stochastic frontier constrained estimation methods, both in terms of runtime and of data capacity.

Although monotonicity constraints can be applied in a simple manner by the specification of sign constraints on the regression coefficients in both parametric and nonparametric settings, estimation of concavity-constrained production frontiers for flexible functional has proven to be more challenging. Imposition of such constraints in the nonparametric setting was discussed by Banker and Mandiratta (1992) and has been conducted using Least Squares approaches using Convex Nonparametric Least Squares (CNLS) (Kuosmanen, 2008). Du, Parmeter and Racine (2013) develop a Kernel regression method that also allows for imposition of a vast array of derivative-based constraints, including global concavity. This presentation discusses an extension to Multi-variate Bayesian Convex Regression (MBCR) developed by Hannah and Dunson (2013) to the setting with inefficiency. This extension offers benefits both in terms of runtime and of data capacity. Moreover, this method includes some extensions to the known Bayesian regression techniques and further enriches the Bayesian literature.


October 4 and 5: College Industry Council on Material Handling Education Semi-annual Meeting in San Diego


The College Industry Council on Material Handling Education (CICMHE) is a group of 15 academic and 8 industry members that work to increase awareness, understanding, exploration, and development of material handling and logistics through projects and events.

CICMHE meet in San Diego as part of the MHI‘s annual conference. The two-day event resulted in a set of projects MHI will fund to promote material handling and logistics education along with allowing considering progress on planning the upcoming events for CICMHE such as Promat.

September 25: The 6th Helsinki Workshop on Efficiency and Productivity Analysis, Helsinki Finland


Bayesian StoNED or Multi-variate Bayesian Convex Regression with inefficiency: Two sides of the same coin.

Bayesian methods allow researchers to simulate regression models to investigate the effects of prior distributional assumptions and modeling restrictions on posterior estimates of model parameters. Here we try to develop a Bayesian version of Stochastic Nonparametric Envelopment of Data (StoNED). There are strong similarities between StoNED and Multi-variate Bayesian Convex Regression (MBCR) proposed by Hannah and Dunson (2013). This talk describes some of these similarities along with some computational results regarding problem size and accuracy of the estimated parameters. We propose an MBCR model with an inefficiency term as a Bayesian StoNED equivalent.