l0-regularized item response theory model for robust ideal point estimation
针对立法者抗议投票导致的估计偏差，提出一种结合L0正则化的快速EM算法，在保持估计精度的同时大幅提升计算速度，并成功识别美国众议院中“小组”成员的极端自由派立场。
Abstract Ideal point estimation methods face a significant challenge when legislators engage in protest voting – strategically voting against their party to express dissatisfaction. Such votes introduce attenuation bias, making ideologically extreme legislators appear artificially moderate. We propose a novel statistical framework that extends the fast EM-based estimation approach\ using ℓ0 regularization method to handle protest votes. Through simulation studies, we demonstrate that our proposed method maintains estimation accuracy even with high proportions of protest votes, while being substantially faster than MCMC-based methods. Applying our method to the 116th and 117th U.S. House of Representatives, we successfully recover the extreme liberal positions of ‘the Squad’, whose protest votes had caused conventional methods to misclassify them as moderates. While conventional methods rank Ocasio-Cortez as more conservative than 69% of Democrats, our method places her firmly in the progressive wing, aligning with her documented policy positions. This approach provides both robust ideal point estimates and systematic identification of protest votes, facilitating deeper analysis of strategic voting behaviour in legislatures.