English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Assessing the technical efficiency of a set of observations requires that theassociated data composed of inputs and outputs are perfectly known. If this isnot the case, then biased estimates will likely be obtained. Data EnvelopmentAnalysis (DEA) is one of the most extensively used mathematical models toestimate efficiency. It constructs a piecewise linear frontier against whichall observations are compared. Since the frontier is empirically defined, anydeviation resulting from low data quality (imperfect knowledge of data or IKD)may lead to efficiency under/overestimation. In this study, we model IKD and,then, apply the so-called Hit \& Run procedure to randomly generate admissibleobservations, following some prespecified probability density functions. Setsused to model IKD limit the domain of data associated with each observation.Any point belonging to that domain is a candidate to figure out as theobservation for efficiency assessment. Hence, this sampling procedure must runa sizable number of times (infinite, in theory) in such a way that it populatesthe whole sets. The DEA technique is used during the execution of eachiteration to estimate bootstrapped efficiency scores for each observation. Weuse some scenarios to show that the proposed routine can outperform some of theavailable alternatives. We also explain how efficiency estimations can be usedfor statistical inference. An empirical case study based on the Portuguesepublic hospitals database (2013-2016) was addressed using the proposed method.

Data Envelopment Analysis models with imperfect knowledge of input and output values: An application to Portuguese public hospitals