---
title: BRM for Predictions with Missing Patterns
url: https://www.ml-quant.com/papers/repec/inm-orijds-v-4-y-2025-i-1-p-85-99/
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:inm:orijds:v:4:y:2025:i:1:p:85-99
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fijds.2022.9016%3Bh%3Drepec%3Ainm%3Aorijds%3Av%3A4%3Ay%3A2025%3Ai%3A1%3Ap%3A85-99
featured: 2025-02-26
citations: unknown
topic: Other
---


# BRM for Predictions with Missing Patterns

The blockwise reduced modeling (BRM) method is introduced to analyze incomplete data, using ensemble models to reduce data imputation and enhance predictive performance.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fijds.2022.9016%3Bh%3Drepec%3Ainm%3Aorijds%3Av%3A4%3Ay%3A2025%3Ai%3A1%3Ap%3A85-99
- Identifier: RePEc:inm:orijds:v:4:y:2025:i:1:p:85-99
- Released: 2025-02-26
- First featured: Quant Letter No. 86 (2025-02-26): https://www.ml-quant.com/issues/2025-02-26/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Other

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