 Quantile DEA: Estimating qDEA-alpha Efficiency Estimates with Conventional Linear ProgrammingJoseph A. Atwood () and
Saleem Shaik
A chapter in Productivity and Inequality, 2018, pp 305-326 from Springer Abstract: Abstract Conventional non-parametric linear programming (LP) based data envelopment analysis (DEA) models have the advantage of being able to estimate multiple input-output efficiency metrics but suffer from sensitivity to outliers and statistical observational noise. Previous observation-deleting approaches to the outlier/noise problem have been somewhat ad hoc usually requiring iterative LP and non-LP problem solving methods. We present the theory and methodology of quantile-DEA (qDEA), similar in concept to quantile-regression, which enables the analyst to directly use LP to obtain efficiency metrics while specifying that no more than ψ-percent of data points can lie external to the efficiency hull. Estimated qDEA-α frontiers encompassing proportion α = 1 − ψ of the data observations are contrasted to order-α frontier estimates. Quantile DEA is shown to be useful in addressing outliers in a study examining changes in relative state level agricultural efficiency measures over time. Keywords: Data envelopment analysis; Partial moments; Outliers; Statistical noise; Quantile DEA (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-68678-3_14 Ordering information: This item can be ordered from DOI: 10.1007/978-3-319-68678-3_14 Access Statistics for this chapter More chapters in Springer Proceedings in Business and Economics from Springer |
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