Results 1 to 10 of about 1,619,914 (141)
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.
Verardi, Vincenzo, Croux, Christophe
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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 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. As sample sizes and the number of predictors increase, interactive methodology becomes less effective.
David W. Scott, Zhipeng Wang 0005
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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 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 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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Robust linear least squares regression [PDF]
, 2011 We consider the problem of robustly predicting as well as the best linear combination of $d$ given functions in least squares regression, and variants of this problem including constraints on the parameters of the linear combination.
Audibert, Jean-Yves, Catoni, Olivier
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Robust Multivariate Regression
Technometrics, 2004 We introduce a robust method for multivariate regression based on robust estimation of the joint location and scatter matrix of the explanatory and response variables. As a robust estimator of location and scatter, we use the minimum covariance determinant (MCD) estimator of Rousseeuw.
Rousseeuw, Peter +3 more
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Robustness of Deepest Regression
Journal of Multivariate Analysis, 2000 The notion of the regression depth is one of the most interesting and important notions recently studied in multivariate analysis. The authors investigate the robustness properties of deepest regressions. It is shown that the deepest regression functional is Fisher-consistent for the conditional median, and has a breakdown value of 1/3 in all ...
Van Aelst, Stefan, Rousseeuw, Peter
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CoRR, 2016 Modern technologies are producing datasets with complex intrinsic structures, and they can be naturally represented as matrices instead of vectors. To preserve the latent data structures during processing, modern regression approaches incorporate the low-rank property to the model and achieve satisfactory performance for certain applications.
Hang Zhang 0010, Fengyuan Zhu, Shixin Li
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