Criticality of predictors in multiple regression.

By R. Azen, David V. Budescu, B. Reiser. 2001

45 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistics and Probability

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Citations in the corpus

All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, methodology): “This set of well-fitting models is identified by repeating an estimation procedure in a series of perturbed samples, using varying levels of regularization (see also Azen et al., 2001). Letham et al. (2016) search for a pair of well-fitting dynamical systems models that give maximally different pred”
An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests (2009, unknown): “…to problems with many predictor values, also distinguishes the random forest variable importance from the otherwise appealing approach of Azen, Budescu, and Reiser (2001) and advanced in Azen and Budescu (2003) for assessing the criticality of a predictor variable, termed dominance…”
A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection (2017, background): “Estimations of absolute and relative importance of variables are not independent from one another and they sometimes need to be performed concomitantly for reliable statistical inferences and predic - tions (Azen, Budescu, & Reiser, 2001).”
All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018, methodology): “across a set of well-tting models. This set of well-tting models is identied by repeating an estimation procedure in a series of perturbed samples, using varying levels of regularization (see also Azen et al., 2001). Letham et al. (2016) search for a pair of well-tting dynamical systems models that ”
History and Use of Relative Importance Indices in Organizational Research (2004, background): “Azen, Budescu, and Reiser (2001) proposed a new approach to comparing predictors in multiple regression, which they termed predictor criticality.”