- Title
- Rejoinder to "A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression"
- Creators
- Lan Wang - University of MiamiBo Peng - Adobe SystemsJelena Bradic - University of California, San DiegoRunze Li - Pennsylvania State UniversityYunan Wu - University of Minnesota
- Publication Details
- Journal of the American Statistical Association, Vol.115(532), pp.1726-1729
- Publisher
- Amer Statistical Assoc
- Number of pages
- 4
- Academic Unit
- Miami Herbert Business School; MHBS - Management Science
- Language
- English
- Resource Type
- Journal article
- Record Identifier
- 991031757746602976
Journal article
Rejoinder to "A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression"
Journal of the American Statistical Association, Vol.115(532), pp.1726-1729
2020-12-10
Metrics
7 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Citation topics
- 9 Mathematics
- 9.92 Statistical Methods
- 9.92.220 Nonparametric Regression
- Web Of Science research areas
- Statistics & Probability
- ESI research areas
- Mathematics
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Source: InCites