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 the 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 powerful, reliable, and fully open access computational package has 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 the 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 the estimation of frontier cost and production functions.
翻译:在生产率和效率分析领域,多变量锥体回归的最近发展以及 convex Qiontial回归和 convex 预测回归等相关技术,缩小了常规确定性非参数回归和随机参数回归方法之间的长期差距。不幸的是,沉重的计算负担和缺乏强大、可靠和完全开放的计算包,减缓了这些先进估算技术向实证实践的传播速度。Python 包 PyStoNED的目的是通过为多变量锥体回归、 convex Qontile回归、 convex 预测回归、 同位素回归、 同位素回归、 数据随机非参数整合及相关方法提供一个可自由获取和方便用户的工具,应对这一挑战。本文介绍了PyStoNED包的教义,并介绍了其应用情况,重点是前沿成本和生产功能的估算。