The elements of statistical learning. 2001

By Trevor J. Hastie, Robert Tibshirani, Jerome H. Friedman. 2001

247 citations recorded by the source corpus.

Subfields: Statistics and Probability, Statistical Learning Theory

How researchers see this paper

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

In Statistics and Probability

1. Peter Carbonetto
2. Matthew Stephens
3. Alexandra Chouldechova
4. Gao Wang
5. Abhishek K. Sarkar
6. Dinggang Shen
7. Ehsan Adeli
8. Jun Zhang

In Statistical Learning Theory

1. Alexandra Chouldechova
2. Dinggang Shen
3. Gao Wang
4. Abhishek K. Sarkar
5. Matthew Stephens
6. Peter Carbonetto
7. Trevor J. Hastie
8. Mingxia Liu

Citations in the corpus

• Generalized Additive Model Selection (2015, methodology): “3% reported in Hastie et al. (2009), as found by step.gam .”
• A simple new approach to variable selection in regression, with application to genetic fine mapping (2018, methodology): “For example, we can compare it with an approach referred to as “Forward-Stagewise” (FS) selection in Hastie et al. (2009) Section 3.3.3 (although subsequent literature often uses this term slightly differently).”
• Landmark-based deep multi-instance learning for brain disease diagnosis (2017, methodology): “The major challenge of voxel-level representations is that they usually lead to the over-fitting problem, since there are only limited ( e.g. , tens or hundreds) subjects with very high ( e.g. , millions) dimensional features ( Friedman et al., 2001 ).”