A1603
Title: Scalable estimation of crossed random effects models via multi-way discretization
Authors: Shota Takeishi - Washington University in St. Louis (United States) [presenting]
Shonosuke Sugasawa - Keio University (Japan)
Abstract: Cross-classified data frequently arise in scientific fields such as education, healthcare, and social sciences. A common modeling strategy is to introduce crossed random effects within a regression framework. However, this approach often encounters serious computational bottlenecks, particularly for non-Gaussian outcomes. A scalable and flexible method is proposed that approximates the distribution of each random effect by a discrete distribution, effectively partitioning the random effects into a finite number of representative values. This approximation allows the model to be expressed as a multi-way discrete structure, which can be efficiently estimated using a simple and fast iterative algorithm. The proposed method accommodates a wide range of outcome models and remains applicable even in settings with more than two-way cross-classification. Consistency and asymptotic normality of the estimator are theoretically established under general settings of classification levels. Simulation studies and real data applications demonstrate the practical performance of the proposed method.
