Statistical inference for exploratory data analysis and model diagnostics

170 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, background): “05” (Buja et al., 2009).”
Graphical inference for infovis (2010, methodology): “This paper motivates the use of these methods for infovis and shows how they can be used with common graphics to provide users with a toolkit to avoid false positives.”
50 Years of Data Science (2017, background): “However, how to make the referee “objectively and automatically reports the score” can be an issue, and one possible answer is the recent development of visual inference (Buja et al. 2009; Majumder, Hofmann, and Cook 2013).”
Visual Inference for the Funnel Plot in Meta-Analysis (2019, methodology): “Visual inference is a formal inferential framework (Buja et al., 2009) which allows researchers to test whether graphically displayed data support a hypothesis or not.”
Visualization in Bayesian workflow (2017, methodology): “Visualization is probably the most important tool in an applied statistician’s toolbox and is an important complement to quantitative statistical procedures (Buja et al., 2009).”