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14 Μαΐ 2023 · Stochastic Frontier Analysis (SFA) Installation The pySFA package is now avaiable on PyPI and the latest development version can be installed from the Github repository pySFA .
Stochastic Frontier Analysis (SFA) Installation The pySFA package is now avaiable on PyPI and the latest development version can be installed from the Github repository pySFA .
pyStoNED is a Python package that provides functions for estimating multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods.
To estimate the function \(f\), one could use the parametric and nonparametric methods or neoclassical and frontier models, of which methods are classified based on the specification of \(f\) and error term \(\varepsilon\) (see Kuosmanen and Johnson, 2010).
29 Αυγ 2024 · pyStoNED is a Python package that provides functions for estimating multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods.
To bridge the gap between these two paradigms, Stochastic nonparametric envelopment of data (StoNED) (Kuosmanen, 2006; Kuosmanen and Kortelainen, 2012) was proposed as a unified framework that combines virtues of DEA and SFA, encompassing both approaches as its restricted special cases.
This paper presents a tutorial of the pyStoNED package and illustrates its application, focusing on the estimation of frontier cost and production functions. KW - multivariate convex regression. KW - nonparametric least squares. KW - frontier estimation. KW - efficiency analysis. KW - stochastic noise. KW - python. M3 - Paper
