pyStoNED: A Python Package for Convex Regression and Frontier Estimation

pyStoNED: A Python Package for Convex Regression and Frontier Estimation

multivariate convex regression, nonparametric least squares, frontier estimation,
efficiency analysis, stochastic noise, Python.

Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning, and related fields. In the field of productivity
and efficiency analysis, recent developments in multivariate convex regression and related techniques such as convex quantile regression and convex expectile regression have
bridged the long-standing gap between the conventional deterministic-nonparametric and
stochastic-parametric methods. Unfortunately, the heavy computational burden and the
lack of a powerful, reliable, and fully open-access computational package have slowed
down the diffusion of these advanced estimation techniques to the empirical practice. The
purpose of the Python package pyStoNED is to address this challenge by providing a
freely available and user-friendly tool for multivariate convex regression, convex quantile
regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. This paper presents a tutorial of the pyStoNED
package and illustrates its application, focusing on estimating frontier cost and production
functions

Sheng Dai

https://www.jstatsoft.org/article/view/v111i06

Zhongnan University of Economics and Law

November 2024

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English

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