Multilevel (Hierarchical) Modeling: What It Can and Cannot Do

作者：Andrew Gelman · 发表于：Technometrics · 年份：2006 · DOI：10.1198/004017005000000661 · 被引用次数：580 · 研究领域：Statistical Methods and Bayesian Inference、Spatial and Panel Data Analysis、Soil Geostatistics and Mapping

Multilevel (hierarchical) modeling is a generalization of linear and generalized linear modeling in which regression coefficients are themselves given a model, whose parameters are also estimated from data. We illustrate the strengths and limitations of multilevel modeling through an example of the prediction of home radon levels in U.S. counties. The multilevel model is highly effective for predictions at both levels of the model, but could easily be misinterpreted for causal inference.