A0770
Title: l0-regularized item response theory model for robust ideal point estimation
Authors: Johan Lim - Seoul National University (Korea, South) [presenting]
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. A novel statistical framework is proposed, called emRIRT-L0, that extends the fast EM-based estimation approach of a prior study to handle protest votes. The method introduces shift parameters with l0-regularization to systematically identify protest votes. Through simulation studies, it is demonstrated that emRIRT-L0 maintains estimation accuracy even with high proportions of protest votes while being substantially faster than MCMC-based methods. Applying the method to the 116th and 117th U.S. House of Representatives, the extreme liberal positions of "the Squad" are successfully recovered, 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, the 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 behavior in legislatures
