---
title: Robust Haberman Linking vs. Invariance Alignment
url: https://www.ml-quant.com/papers/repec/gam-jstats-v-8-y-2025-i-1-p-3-d-1559039/
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:gam:jstats:v:8:y:2025:i:1:p:3-:d:1559039
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2571-905X%2F8%2F1%2F3%2Fpdf%3Bh%3Drepec%3Agam%3Ajstats%3Av%3A8%3Ay%3A2025%3Ai%3A1%3Ap%3A3-%3Ad%3A1559039
featured: 2025-01-08
citations: unknown
topic: Asset Pricing & Factors
---


# Robust Haberman Linking vs. Invariance Alignment

The article finds that robust Haberman linking performs better than invariance alignment for factor models when item intercepts are used, with varying results for different loss functions.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2571-905X%2F8%2F1%2F3%2Fpdf%3Bh%3Drepec%3Agam%3Ajstats%3Av%3A8%3Ay%3A2025%3Ai%3A1%3Ap%3A3-%3Ad%3A1559039
- Identifier: RePEc:gam:jstats:v:8:y:2025:i:1:p:3-:d:1559039
- Released: 2025-01-08
- First featured: Quant Letter No. 81 (2025-01-08): https://www.ml-quant.com/issues/2025-01-08/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

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