Post-averaging inference for optimal model averaging estimator in generalized linear models
研究了广义线性模型中最优模型平均估计量的渐近分布，提出了一种基于模拟的置信区间估计方法，并通过蒙特卡洛模拟和赛车数据集验证了其有效性。
.This article considers the problem of post-averaging inference for optimal model averaging estimators in a generalized linear model (GLM). We establish the asymptotic distributions of optimal model averaging estimators for GLMs. The asymptotic distributions of the model averaging estimators are nonstandard, depending on the configuration of the penalty term in the weight choice criterion. We also propose a feasible simulation-based confidence interval estimator and investigate its asymptotic properties rigorously. Monte Carlo simulations verify the usefulness of our theoretical results, and the proposed methods are employed to analyze a stock car racing dataset.