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

By Andrew Gelman. 2006

564 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Statistics and Probability

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Citations in the corpus

Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021, background): “the same person (Gelman 2006)—and they are well suited to analyzing performance for groups based on multiple factors.”
A Framework for Understanding Unintended Consequences of Machine Learning (2019, methodology): “…learning meth-ods , such as multitask learning, that parameterize different groups differently in the model deﬁnition and facilitate learning multiple simpler functions that take into account group differences (Gelman, 2006; Dwork et al., 2017; Suresh, Gong, and Guttag, 2018; Oneto et al., 2018).”
Hierarchical generalized additive models in ecology: an introduction with mgcv (2019, unknown): “Hierarchical generalized linear models ( Gelman, 2006 ; HGLMs; also referred to as generalized linear mixed effect models, multilevel models, etc.; e.g., Bolker et al., 2009 ) are an extension of regression modeling that allows the inclusion of terms in the model that account for structure in the…”
Measuring Sample Quality with Diffusions (2016, methodology): “In the notation of Example 2, we conduct a Bayesian Huber regression analysis (c = 1) of the log radon levels in 1, 190 Minnesota households [29] as a function of the log amount of uranium in the county, an indicator of whether the radon reading was performed in a basement, and an intercept term.”
Functional traits as predictors of vital rates across the life cycle of tropical trees (2016, methodology): “In this case, our initial analyses (eqn 1) are less biased for inference and yield the greatest predictive accuracy (Gelman 2006).”
Environmental Income and Rural Livelihoods: A Global-Comparative Analysis (2014, background): “16 In contrast to a standard cross-sectional regression approach, where varying intercepts or coefficients are introduced through dummy variables and interaction terms, multilevel models allow us to simultaneously and efficiently estimate group-level effects and predictors (Gelman, 2006).”
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors (2020, unknown): “(Gelman, 2006) after replacing θ by n copies θ1,.”
Crisis of trust: Socio-economic determinants of Europeans’ confidence in government (2017, methodology): “We address concerns about intra-country correlations between respondents in the same country by using robust standard errors, clustered by country (Gelman, 2006; Primo et al., 2007).”