Modern Methods for Robust Regression
By Robert Andersen. 2007
368 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7487ed158eb30ff710841495678510c4bacfff8d
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In Statistics and Probability
- C. Wasley
- Miguel Minutti-Meza
- Martha White
- Germaine Cornelissen
- Meng Yang
- Cristobal Young
- Ji Zhang
- Sanjiv Singh
Citations in the corpus
- LOAM: Lidar Odometry and Mapping in Real-time (2014, methodology): “The motion estimation is adapted to a robust fitting [27].”
- Millionaire Migration and Taxation of the Elite (2016, background): “D examination of influential observations (in this case, sets of observations associated with each state) (Andersen 2008; Cook 1977).”
- Robust sparse coding for face recognition (2011, methodology): “Inspired by the robust regression theory [1, 10], we design the signal fidelity term as an MLE-like estimator, which minimizes some function (associated with the distribution of the coding residuals) of the coding residuals.”
- Influential Observations and Inference in Accounting Research (2019, positive): “Estimating bootstrap-based clustered standard errors is consistent with the findings of Cameron et al. (2008) and the recommendations of Andersen (2008, 71).”
- Board Hierarchy, Independent Directors, and Firm Value: Evidence from China (2016, background): “Anderson, Mansi, and Reeb (2004) document that a higher”
- Does income growth relocate ecological footprint (2016, methodology): “(6)For a detailed explanation of robust regression techniques and other weighting functions, see Andersen (2008).”
- Cosinor-based rhythmometry (2014, methodology): “Many of the conventional methods of data analysis depend on similar assumptions, with the exception of robust non-parametric techniques [10,57-61].”
- A general framework for reducing variance in agent evaluation (2010, methodology): “Some robust alternatives to least squares include L-estimators, such as least absolute value regression (L1-norm), least median of squares regression and least trimmed squares regression; R-estimators; and M -estimators, such as iteratively reweighted least squares [Andersen, 2007].”