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.

翻译：我们提出了一种用于时间依赖聚类的鲁棒特征加权跳变模型。该模型通过惩罚项促进状态随时间平滑转移，并采用Tukey双权损失函数实现鲁棒性。一个附加参数控制不同状态下特征权重的变异性，使模型能够为每个特征分配状态特定的相关性。仿真结果表明，该方法能准确恢复真实聚类序列并可靠识别相关特征，在存在异常值的情况下性能优于对比方法。最后，我们通过两个实证应用进行验证：其一是1998-2000年科索沃冲突相关杀人案数量分析，其二是1949-2024年十二个欧洲国家宏观经济表现研究。