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• W2203837399 abstract "The field of production frontier estimation is divided between the parametric Stochastic Frontier Analysis (SFA) and the deterministic, nonparametric Data Envelopment Analysis (DEA). This paper explores an amalgam of DEA and SFA that melds a nonparametric frontier with a stochastic composite error. Our model imposes the standard SFA assumptions for the inefficiency and noise terms. The frontier is estimated nonparametrically, imposing monotonicity and convexity as in DEA. For estimation, we propose two alternative methods based on shape constrained nonparametric least squares. The performance of the proposed estimation techniques is examined using Monte Carlo simulations and an illustrative application." @default.
• W2203837399 created "2016-06-24" @default.
• W2203837399 creator A5014311971 @default.
• W2203837399 creator A5035178097 @default.
• W2203837399 date "2007-01-01" @default.
• W2203837399 modified "2023-10-15" @default.
• W2203837399 title "Stochastic Nonparametric Envelopment of Data: Cross-Sectional Frontier Estimation Subject to Shape Constraints" @default.
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• W2203837399 doi "https://doi.org/10.2139/ssrn.983882" @default.
• W2203837399 hasPublicationYear "2007" @default.
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