Consistent and scalable composite likelihood estimation of probit models with crossed random effects
交叉随机效应 probit 模型的一致且可扩展复合似然估计
Summary Estimation of crossed random effects models commonly incurs computational costs that grow faster than linearly in the sample size $ N $, often as fast as $ \Omega(N^{3/2}) $, making them unsuitable for large datasets. For non-Gaussian responses, integrating out the random effects to obtain a marginal likelihood poses significant challenges, especially for high-dimensional integrals for which the Laplace approximation may not be accurate. In this article we develop a…