Data Analysis Using Regression and Multilevel/Hierarchical Models: Understanding and summarizing the fitted models

By Andrew Gelman, Jennifer L. Hill. 2006

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Citations in the corpus

Stan: A Probabilistic Programming Language (2017, methodology): “org/, links to the Stan manual, example models including translations of the BUGS examples and the models from (Gelman and Hill 2007), getting started instructions and full documentation for Stan’s interfaces for the command line shell (CmdStan), Python (PyStan), and R (RStan), and the source code r”
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (2017, methodology): “and for regression modeling, nearest-neighbor matching, interpolation etc were adopted when the values are MAR (see Chapter 25 of [12], [18]).”
The Problem of Infra-marginality in Outcome Tests for Discrimination (2016, positive): “earch and hit rates to infer the eective racespecic standards of evidence that ocers apply when deciding whom to search, an approach we formalize via a hierarchical Bayesian latent variable model [Gelman and Hill, 2006]. In the process of developing this test, we demonstrate that the issues with ben”
Guidelines for a graph-theoretic implementation of structural equation modeling (2012, background): “The accommoda-tions for these complexities that have been developed in statistical modeling (e.g., Gelman and Hill 2007, Zuur et al. 2009) can typically be brought into SE models, though an additional element of care is needed to accommodate the requirements for causal interpretations.”
A Bayesian hierarchical model for urban air quality prediction under uncertainty (2008, methodology): “Alternatively, the predicators can be centered ðxi;qÞ to eliminate the correlations of model coefficients as xi;q 1⁄4 xi;q ð P31 i1⁄41 xi;qÞ=31(Gelman and Hill, 2007).”
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, background): “Many extensions of the linear regression model exist [45,25,38] and new extensions are proposed until today [26,14,27,117].”
Simple and Scalable Response Prediction for Display Advertising (2014, background): “See also [Gelman and Hill 2006] for a review of multi-level hierarchical models and [Agarwal et al.”