Variance estimation in high-dimensional linear models
By Lee H. Dicker. 2014
110 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/fc206fa6f7c7d2b8538bcd443f5ecb68717710aa
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jafar Jafarov
- Robert Tibshirani
- Sourav Chatterjee
- Jerome H. Friedman
- S. Reid
- Lee H. Dicker
- Trevor J. Hastie
- Peter Buhlmann
In Molecular Biology
- Robert Tibshirani
- Jerome H. Friedman
- Sourav Chatterjee
- Jafar Jafarov
- S. Reid
- Jianqing Fan
- Bradley Efron
- Trevor J. Hastie
In Statistics and Probability
- Jafar Jafarov
- Robert Tibshirani
- Sourav Chatterjee
- Stefan Wager
- Jerome H. Friedman
- Lee H. Dicker
- Trevor J. Hastie
- Susan Athey
Citations in the corpus
- Prediction error of cross-validated Lasso (2015, methodology): “There are a number of proposed methods for error variance estimation in Lasso [8, 11, 28, 31, 32, 33].”
- Ridge regression and asymptotic minimax estimation over spheres of growing dimension (2016, background): “[6] and Dalalyan and Chen [20] on estimating β in high dimensions when σ(2) is unknown); Dicker [25] considers estimating σ(2) when d > n and β is not sparse.”
- A Study of Error Variance Estimation in Lasso Regression (2013, methodology): “Dicker (2014) takes a different tack.”