Predictive inference with random forests: A new perspective on classical analyses

@article{McAlexander2020PredictiveIW,
  title={Predictive inference with random forests: A new perspective on classical analyses},
  author={Richard J. McAlexander and L. Mentch},
  journal={Research \& Politics},
  year={2020},
  volume={7}
}
Despite the number of problems that can occur when core model assumptions are violated, nearly all quantitative political science research relies on inflexible regression models that require a linear relationship between dependent and independent variables for valid inference. We argue that nonparametric statistical learning methods like random forests are capable of combining the benefits of interpretability and flexibility. Recent work has shown that under suitable regularity conditions… Expand
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SHOWING 1-10 OF 18 REFERENCES
Formal Hypothesis Tests for Additive Structure in Random Forests
  • L. Mentch, G. Hooker
  • Mathematics, Computer Science
  • Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
  • 2017
Scalable and Efficient Hypothesis Testing with Random Forests
p-Values for High-Dimensional Regression
Consistency of Random Forests
Asymptotic Distributions and Rates of Convergence for Random Forests and other Resampled Ensemble Learners
Confidence intervals for random forests: the jackknife and the infinitesimal jackknife
Estimation and Accuracy After Model Selection
  • B. Efron
  • Mathematics, Medicine
  • Journal of the American Statistical Association
  • 2014
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