提出鲁棒时序聚类模型，能自动识别关键特征并抗异常值。
Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering
- 用平滑惩罚和Tukey损失提升时序聚类稳定性。
- 在模拟中准确恢复真实聚类序列，优于现有方法。
- 适合处理含异常值的长期时序数据，如经济或冲突数据。
我们提出一种用于时变聚类的鲁棒特征加权跳跃模型。通过惩罚项促进时间上转移的平滑性，利用Tukey双权重损失函数实现鲁棒性。新增参数控制特征权重在不同状态间的可变性，使模型能为每个特征分配状态相关的显著性。模拟结果显示，该方法能准确恢复真实聚类序列，并可靠识别相关特征，在存在异常值时表现优于对比方法。最后，我们在两个实证应用中验证了模型有效性：一是1998-2000年科索沃冲突相关谋杀案件数分析，二是1949-2024年十二个欧洲国家宏观经济表现研究。
原文摘要 · Abstract (English)
We propose a robust feature-weighted jump model for time-dependent clustering. A penalty is used to encourage smoothness of transitions over time, while robustness is achieved through the use of a Tukey's biweight loss function. An additional parameter controls the variability of feature weights across states, allowing the model to assign state-specific relevance to each feature. We illustrate in simulation how the method accurately recovers the true cluster sequence and reliably identifies relevant features, outperforming competing approaches, particularly in the presence of outliers. We conclude with two empirical applications, one on the number of conflict-related homicides in Kosovo in the period 1998-2000, and another on macroeconomic performance of twelve European countries in the period 1949-2024.
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