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Andrew L. Johnson

Bio: Andrew L. Johnson is an academic researcher from University of Derby. The author has contributed to research in topics: Data envelopment analysis & Estimator. The author has an hindex of 28, co-authored 107 publications receiving 2723 citations. Previous affiliations of Andrew L. Johnson include Texas A&M University & Aalto University.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors measured annual shell growth rates of a 374-year-old (radiometrically confirmed) bivalve mollusk specimen of Arctica islandica (Linnaeus).

315 citations

Journal ArticleDOI
TL;DR: It is shown that DEA can be alternatively interpreted as nonparametric least-squares regression subject to shape constraints on the frontier and sign constraints on residuals, which reveals the classic parametric programming model by Aigner and Chu as a constrained special case of DEA.
Abstract: Data Envelopment Analysis (DEA), a nonparametric mathematical programming approach to productive efficiency analysis, envelops all observed data. In this paper we show that DEA can be interpreted as nonparametric least squares regression subject to shape constraints on frontier and sign constraints on residuals, and that classic parametric programming model is a constrained special case of DEA. Applying these insights, we present a nonparametric variant of the corrected ordinary least squares (COLS) method. We show that this new method, which we term corrected concave nonparametric least squares (C2NLS) is consistent and asymptotically unbiased. The linkages established in this paper contribute to further integration of the econometric and linear programming approaches.

194 citations

Journal ArticleDOI
TL;DR: In this article, a nonparametric variant of the corrected ordinary least-squares (COLS) method, referred to as corrected concave non-parametric least squares (C2NLS), is presented.
Abstract: Data envelopment analysis (DEA) is known as a nonparametric mathematical programming approach to productive efficiency analysis. In this paper, we show that DEA can be alternatively interpreted as nonparametric least-squares regression subject to shape constraints on the frontier and sign constraints on residuals. This reinterpretation reveals the classic parametric programming model by Aigner and Chu [Aigner, D., S. Chu. 1968. On estimating the industry production function. Amer. Econom. Rev.58 826--839] as a constrained special case of DEA. Applying these insights, we develop a nonparametric variant of the corrected ordinary least-squares (COLS) method. We show that this new method, referred to as corrected concave nonparametric least squares (C2NLS), is consistent and asymptotically unbiased. The linkages established in this paper contribute to further integration of the econometric and axiomatic approaches to efficiency analysis.

170 citations

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TL;DR: A new semi-nonparametric one-stage estimator for the coefficients of the contextual variables that directly incorporates contextual variables to the standard DEA problem is developed, and evidence from Monte Carlo simulations suggests that the new 1-DEA estimator performs systematically better than the conventional 2-DEa estimator both in deterministic and noisy scenarios.

128 citations

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TL;DR: This paper discusses the four most widely-used approaches to guide variable specification in DEA and analyzes efficiency contribution measure, principal component analysis, regression-based test, and bootstrapping for variable selection via Monte Carlo simulations to determine each approach's advantages and disadvantages.

126 citations


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Book
01 Jan 2009

8,216 citations

Journal ArticleDOI
TL;DR: The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active.
Abstract: This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.

482 citations

Journal ArticleDOI
TL;DR: This paper reviews studies on network DEA by examining the models used and the structures of the network system of the problem being studied, and highlights some directions for future studies from the methodological point of view.

446 citations