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Robust Multiple Regression [PDF]
Entropy, 2021 As modern data analysis pushes the boundaries of classical statistics, it is timely to reexamine alternate approaches to dealing with outliers in multiple regression.
David W. Scott, Zhipeng Wang
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Robust Geodesic Regression [PDF]
International Journal of Computer Vision, 2022 This paper studies robust regression for data on Riemannian manifolds. Geodesic regression is the generalization of linear regression to a setting with a manifold-valued dependent variable and one or more real-valued independent variables. The existing work on geodesic regression uses the sum-of-squared errors to find the solution, but as in the ...
Ha Young Shin, Hee-Seok Oh
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Robust Joint Sparse Uncorrelated Regression [PDF]
Jisuanji kexue, 2022 Common unsupervised feature selection methods only consider the selection of discriminative features,while ignoring the redundancy of features and failing to consider the problem of small classes,which affect the classification performance.Based on this ...
LI Zong-ran, CHEN XIU-Hong, LU Yun, SHAO Zheng-yi
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Flexible Robust Mixture Regression Modeling
Revstat Statistical Journal, 2022 This paper provides a flexible methodology for the class of finite mixture of regressions with scale mixture of skew-normal errors (SMSN-FMRM) introduced by [42], relaxing the constraints imposed by the authors during the estimation process.
Marcus G. Lavagnole Nascimento +1 more
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Robust Phylogenetic Regression
Systematic Biology, 2022 Abstract Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships.
Richard Adams +3 more
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Robust boosting for regression problems [PDF]
Computational Statistics & Data Analysis, 2021 Gradient boosting algorithms construct a regression predictor using a linear combination of ``base learners''. Boosting also offers an approach to obtaining robust non-parametric regression estimators that are scalable to applications with many explanatory variables.
Xiaomeng Ju, Matías Salibián Barrera
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Axioms, 2023 Under classical statistics, research typically relies on precise data to estimate the population mean when auxiliary information is available. Outliers can pose a significant challenge in this process.
Abdullah Mohammed Alomair, Usman Shahzad
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Robust Regression and Lasso [PDF]
IEEE Transactions on Information Theory, 2010 Lasso, or $\ell^1$ regularized least squares, has been explored extensively for its remarkable sparsity properties. It is shown in this paper that the solution to Lasso, in addition to its sparsity, has robustness properties: it is the solution to a robust optimization problem. This has two important consequences.
Huan Xu 0001 +2 more
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Robust Regression in Stata [PDF]
SSRN Electronic Journal, 2008 In regression analysis, the presence of outliers in the dataset can strongly distort the classical least-squares estimator and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the rreg and qreg commands. Unfortunately,
Verardi, Vincenzo, Croux, Christophe
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Chemical Oxygen Demand (COD) Estimation in Petrochemical Industry Wastewater Effluent via Robusted Regression [PDF]
علوم و مهندسی آب و فاضلاب, 2018 In order to increase the quality of industrial wastewater treatment and better manage of them, their approach should be simple and accurate for estimating process.
Milad Abuzari +2 more
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