The Highly Adaptive Lasso Estimator

@article{Benkeser2016TheHA,
  title={The Highly Adaptive Lasso Estimator},
  author={David C. Benkeser and Mark J. van der Laan},
  journal={2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)},
  year={2016},
  pages={689-696}
}
  • D. Benkeser, M. J. Laan
  • Published 2016
  • Computer Science, Mathematics, Economics
  • 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Estimation of a regression functions is a common goal of statistical learning. We propose a novel nonparametric regression estimator that, in contrast to many existing methods, does not rely on local smoothness assumptions nor is it constructed using local smoothing techniques. Instead, our estimator respects global smoothness constraints by virtue of falling in a class of right-hand continuous functions with left-hand limits that have variation norm bounded by a constant. Using empirical… 

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