143 citations · 146 across the 3 of their papers we have counts for
5 papers
Using leave-one-out cross-validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale
Swen Kuh, Lauren Kennedy, Qixuan Chen +1
In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on detail…
Bayesian Workflow
Andrew Gelman, Aki Vehtari, Daniel Simpson +7
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilis…
Improving multilevel regression and poststratification with structured priors
Yuxiang Gao, Lauren Kennedy, Daniel Simpson +1
A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multi…
Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample
Lauren Kennedy, Andrew Gelman
Psychology research focuses on interactions, and this has deep implications for inference from non-representative samples. For the goal of estimating average treatment effects, we…
The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling
Lauren Kennedy, Daniel Simpson, Andrew Gelman
Cognitive modelling shares many features with statistical modelling, making it seem trivial to borrow from the practices of robust Bayesian statistics to protect the practice of ro…