STOCHASTIC DEA Chapter uri icon

abstract

  • © 2017 John Wiley & Sons Ltd. All rights reserved. Many researchers have generalized data envelopment analysis (DEA) to the stochastic setting. Each method has different assumptions and data requirements. This chapter reviews several of the most common stochastic DEA methods, comparing them on the basis of data requirements and assumptions. Regression-based methods receive particular attention because they include classical noise models built on statistical results such as laws of large numbers. The noise models relax the need for additional data and are based on assumptions that are likely to hold in a wide variety of applications.

author list (cited authors)

  • Johnson, A. L.

citation count

  • 1

complete list of authors

  • Johnson, Andrew L

Book Title

  • Advances in DEA Theory and Applications

publication date

  • May 2017