William W. Cooper

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This paper discusses returns to scale (RTS) in data envelopment analysis (DEA) for each of the presently available types of models. The BCC and CCR models are treated in input oriented forms while the multiplicative model is treated in output oriented form. (This distinction is not pertinent for the additive model which simultaneously maximizes outputs and(More)
The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their ‘‘deterministic equivalents.’’ This leads to a class of non-linear problems. However, it is shown(More)
This article introduces a neural network artificial intelligence model as an early warning system for predicting insurer insolvency. In order to investigate a firm's propensity toward insolvency, a feed forward, back-propagation methodology is applied to financial data two years prior to insolvency for a sample of U.S. property-liability insurers that(More)
This paper studies the effects of heteroscedasticity on the following five types of estimators: (1) Data Envelopment Analysis (DEA) per se as well as DEA joined to regression forms, (2) Corrected Ordinary Least Squares based on maximum residual (COLS-R), (3) Corrected Ordinary Least Squares based on moments of residuals (COLS-M), (4) Maximum Likelihood(More)