Dual-Homotopy Framework for Constrained EM Algorithm

Fuente: arXiv
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Hauptverfasser: Choi, Jisoo, Oh, Hee-Seok
Format: Preprint
Veröffentlicht: 2026
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author Choi, Jisoo
Oh, Hee-Seok
author_facet Choi, Jisoo
Oh, Hee-Seok
contents We propose a new constrained EM algorithm that is applicable to general constrained estimation problems. The proposed method is based on a novel framework, the `dual-homotopy framework,' which combines deterministic annealing EM with a barrier-based optimization, enabling stable estimation under parameter constraints. Building on this framework, we further introduce an adaptive constrained EM algorithm that preserves likelihood monotonicity, regardless of the underlying distributional form or the specific structure of the constraints. Through simulation studies and a real-data analysis, both under parameter constraints, we demonstrate that the proposed algorithm yields more stable and accurate estimates than existing methods, including the standard EM algorithm.
format Preprint
id arxiv_https___arxiv_org_abs_2605_05798
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Dual-Homotopy Framework for Constrained EM Algorithm
Choi, Jisoo
Oh, Hee-Seok
Methodology
We propose a new constrained EM algorithm that is applicable to general constrained estimation problems. The proposed method is based on a novel framework, the `dual-homotopy framework,' which combines deterministic annealing EM with a barrier-based optimization, enabling stable estimation under parameter constraints. Building on this framework, we further introduce an adaptive constrained EM algorithm that preserves likelihood monotonicity, regardless of the underlying distributional form or the specific structure of the constraints. Through simulation studies and a real-data analysis, both under parameter constraints, we demonstrate that the proposed algorithm yields more stable and accurate estimates than existing methods, including the standard EM algorithm.
title Dual-Homotopy Framework for Constrained EM Algorithm
topic Methodology
url https://arxiv.org/abs/2605.05798