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Andrew L. Johnson
Researcher at University of Derby
Publications - 110
Citations - 3166
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.
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Climate records from a bivalved Methuselah (Arctica islandica, Mollusca; Iceland)
Bernd R. Schöne,Jens Fiebig,Miriam Pfeiffer,Renald Gleβ,Jonathan A. Hickson,Andrew L. Johnson,Wolfgang Dreyer,Wolfgang Oschmann +7 more
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).
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Data Envelopment Analysis as Nonparametric Least Squares Regression
Timo Kuosmanen,Andrew L. Johnson +1 more
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.
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Data Envelopment Analysis as Nonparametric Least-Squares Regression
Timo Kuosmanen,Andrew L. Johnson +1 more
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.
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One-stage and two-stage DEA estimation of the effects of contextual variables
Andrew L. Johnson,Timo Kuosmanen +1 more
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.
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Guidelines for using variable selection techniques in data envelopment analysis
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.