In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...

Robust Regression and Outlier Detection is a book on robust statistics, particularly focusing on the breakdown point of methods for robust regression...

review of novelty detection. Signal Processing, 99, 215-249. Rousseeuw, P; Leroy, A. (1996), Robust Regression and Outlier Detection (3rd ed.), John Wiley...

location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers. Another motivation...

Advanced Research Computing. Robust statistics – Data sets used in Robust Regression and Outlier Detection (Rousseeuw and Leroy, 1968). Provided online...

is more robust in the presence of outliers, leading to quantile regression Nonparametric regression, requires a large number of observations and is computationally...

two or more machine learning algorithms on a specific classification or regression task. The algorithms within the ensemble model are generally referred...

Rousseeuw, Peter J.; Leroy, Annick M. (2003), Robust Regression and Outlier Detection, Wiley Series in Probability and Mathematical Statistics, vol. 516, Wiley...

robust estimation and hypothesis testing. Academic Press. Devlin, Susan J.; Gnanadesikan, R.; Kettenring J.R. (1975). "Robust estimation and outlier detection...

classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, while regression algorithms...

Liu, J.; Yin, W. (2021). "Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection". Advances in Neural Information...

outliers, when outliers are to be accorded no influence[clarify] on the values of the estimates. Therefore, it also can be interpreted as an outlier detection...

JSTOR 2288718. Rousseeuw, P. J.; Leroy, A. M. (2005) [1987]. Robust Regression and Outlier Detection. Wiley. doi:10.1002/0471725382. ISBN 978-0-471-85233-9...

analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals...

the elements of x, and they may also be useful in regression, in selecting a subset of variables from x, and in outlier detection. Property 3: (Spectral...

Data points with large residuals (outliers) and/or high leverage may distort the outcome and accuracy of a regression. Cook's distance measures the effect...

Covariance Determinant methods for robust scatter matrices. This work led to his book Robust Regression and Outlier Detection with Annick Leroy. With Leonard...

nearest neighbor. The k-NN algorithm can also be generalized for regression. In k-NN regression, also known as nearest neighbor smoothing, the output is the...

William Sealy Gosset Regression Deletion Diagnostics R docs Allen J. Pope (1976), "The statistics of residuals and the detection of outliers", U.S. Dept. of...

training of a linear regression model with input perturbations restricted by the infinity-norm closely resembles Lasso regression, and that adversarial training...

expected to have many outliers in the point sets to match. SCS delivers high robustness against outliers and can surpass ICP and CPD performance in the...

(IQR), is designed to be robust to outliers. It scales features using the median and IQR as reference points instead of the mean and standard deviation: x...

continuous values (typically real numbers) are called regression trees. More generally, the concept of regression tree can be extended to any kind of object equipped...

decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during...

pp. 176 and 200–201. ISBN 978-0-387-35212-1. MR 0395032. Rousseeuw, Peter J.; Leroy, Annick M. (2003), Robust Regression and Outlier Detection, Wiley Series...

opposed to variance). It can also improve the stability and accuracy of ML classification and regression algorithms. Hence, it is prevalent in supervised learning...

or coefficients of the regression equation. Mahalanobis distance is also used to determine multivariate outliers. Regression techniques can be used to...

Regression diagnostic Regression dilution Regression discontinuity design Regression estimation Regression fallacy Regression-kriging Regression model validation...

(SOM) Logistic regression Ordinary least squares regression (OLSR) Linear regression Stepwise regression Multivariate adaptive regression splines (MARS)...

2006 to train robust controllers in a game theoretic sense, by alternating the iterations between a minimizer policy, the controller, and a maximizer policy...