Fitting Multivariate Multilevel Models under Informative Sampling
针对信息性抽样问题，提出一种多元多层次正态建模方法，通过提取样本模型并用贝叶斯方法拟合，在巴西全国评估数据模拟中验证了其改进推断的效果。
Abstract A model-dependent approach for multivariate multilevel normal modelling that accounts for informative sampling of group and unit level population elements is developed. The approach involves extracting the multilevel model holding for the sample data, given the selected sample, as a function of the corresponding population model and the sample selection probabilities, and then fitting the resulting sample model using Bayesian methods. A model-based simulation study is carried out to study the performance of our approach under one scenario motivated by a Brazilian nationwide proficiency assessment exercise. Results indicate that our approach enables improved inference under the informative sampling considered.