The goal of this work is the development of a methodology and a computational environment to enable a Pareto frontier analysis for the preliminary design optimization of a wing/horizontal tail/fuselage aircraft configuration.
Contrary to traditional regression analysis that estimates the average parametric relationship between cognitive scores and age, stochastic frontier analysis allows estimation of the parametric function that describes all individual scores.
On the other hand, this is a common problem when FADN data are used, and despite this, a wide collection of literature has been produced on frontier analysis using this sort of data, explicitly or implicitly assuming that the evolution of the FADN sample is aligned with the evolution of a certain sector in each country (Madau 2011, 2015; Oude Lansink et al. 2002).
The main advantages of stochastic frontier analysis are that it allows estimation of parametric function relating cognitive scores and driving factors at the boundary and disentangles frontier noise and distance to frontier components, as well as testing the effect of potential factors on these distances simultaneously.
The main advantage of frontier analysis is that it offers a methodology specifically conceived to measure performance in a benchmark setting built upon all available observations.
From the methodological point of view and in the framework of nonparametric frontier analysis, the proposed future research can be approached either by double bootstrap semi-parametric approaches (Simar & Wilson 2007) or by the more flexible conditional directional distance functions (Simar & Vanhems 2012; Daraio & Simar 2014).
In cases of stochastic frontier analysis estimates with Translog (and therefore quadratic forms) the curvature violation is much more feasible than a Cobb Douglas production function.
For estimation purposes, we opted in favor of the frontier analysis techniques used in economic modeling.
This ability was conceptualised as one of technical production efficiency ("economic" efficiency ≡ output/input, scaled [0,1] [ 21]), estimated by the econometric technique of stochastic frontier analysis (SFA) [ 22].
In contrast to parametric methods such as stochastic frontier analysis (SFA), DEA has the advantage of not requiring that any assumptions be made about the functional form of the production or cost frontier.
