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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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Application of robust regression in translational neuroscience studies with non-Gaussian outcome data [PDF]
Frontiers in Aging Neuroscience, 2023 Linear regression is one of the most used statistical techniques in neuroscience, including the study of the neuropathology of Alzheimer’s disease (AD) dementia. However, the practical utility of this approach is often limited because dependent variables
Michael Malek-Ahmadi +15 more
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Robust regression in Stata. [PDF]
SSRN Electronic Journal, 2008 In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results.
Croux, Christophe, Verardi, Vincenzo
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Robust Distributed High-Dimensional Regression: A Convoluted Rank Approach [PDF]
EntropyThis paper investigates robust high-dimensional convoluted rank regression in distributed environments. We propose an estimation method suitable for sparse regimes, which remains effective under heavy-tailed errors and outliers, as it does not impose ...
Mingcong Wu
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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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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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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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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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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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