• Corpus ID: 190000032

(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data

@article{Pospisil2019fRFCDERF,
  title={(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data},
  author={Taylor Pospisil and Ann B. Lee},
  journal={arXiv: Computation},
  year={2019}
}
Random forests is a common non-parametric regression technique which performs well for mixed-type unordered data and irrelevant features, while being robust to monotonic variable transformations. Standard random forests, however, do not efficiently handle functional data and runs into a curse-of dimensionality when presented with high-resolution curves and surfaces. Furthermore, in settings with heteroskedasticity or multimodality, a regression point estimate with standard errors do not fully… 

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