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pyStoNED : A Python Package for Convex Regression and Frontier Estimation




TekijätDai, Sheng; Fang, Yu-Hsueh; Lee, Chia-Yen; Kuosmanen, Timo

KustantajaFoundation for Open Access Statistics

KustannuspaikkaLOS ANGELES

Julkaisuvuosi2024

JournalJournal of Statistical Software

Tietokannassa oleva lehden nimiJOURNAL OF STATISTICAL SOFTWARE

Lehden akronyymiJ STAT SOFTW

Vuosikerta111

Numero6

Aloitussivu1

Lopetussivu43

Sivujen määrä43

ISSN1548-7660

DOIhttps://doi.org/10.18637/jss.v111.i06

Verkko-osoitehttps://www.jstatsoft.org/article/view/v111i06

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/477928536


Tiivistelmä
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 velopment 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.

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Last updated on 2025-27-01 at 19:41