---
title: Estimating Convex Production Technologies
url: https://www.ml-quant.com/papers/repec/eee-ejores-v-323-y-2025-i-1-p-224-240/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:ejores:v:323:y:2025:i:1:p:224-240
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0377221724008993%3Bh%3Drepec%3Aeee%3Aejores%3Av%3A323%3Ay%3A2025%3Ai%3A1%3Ap%3A224-240
featured: 2025-03-05
citations: unknown
topic: ML & AI Methods
---


# Estimating Convex Production Technologies

The research adapts Stochastic Gradient Boosting for Data Envelopment Analysis to estimate production possibility sets, reducing overfitting and satisfying shape constraints, as proven by simulations and a PISA example.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0377221724008993%3Bh%3Drepec%3Aeee%3Aejores%3Av%3A323%3Ay%3A2025%3Ai%3A1%3Ap%3A224-240
- Identifier: RePEc:eee:ejores:v:323:y:2025:i:1:p:224-240
- Released: 2025-03-05
- First featured: Quant Letter No. 87 (2025-03-05): https://www.ml-quant.com/issues/2025-03-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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