双同调框架用于约束EM算法
Dual-Homotopy Framework for Constrained EM Algorithm
AI总结：
本文提出一种新的约束EM算法，结合确定性退火EM与障碍优化，实现参数约束下的稳定估计，并通过模拟和实际数据证明其比传统EM算法更稳定准确。
摘要：
我们提出了一种新的约束EM算法，适用于一般约束估计问题。所提方法基于新颖的`双同调框架`，结合确定性退火EM与基于障碍的优化，实现参数约束下的稳定估计。在此框架基础上，进一步引入一种自适应约束EM算法，保持似然单调性，无论底层分布形式或约束结构如何。通过模拟研究和实际数据分析，证明所提算法在参数约束下比现有方法，包括标准EM算法，产生更稳定和准确的估计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
