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

called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which...

Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...

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

regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression...

significant advance in their applicability. Robust confidence intervals Robust regression Unit-weighted regression Sarkar, Palash (2014-05-01). "On some connections...

maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers...

Median absolute deviation Ordinary least squares Robust regression "Least Absolute Deviation Regression". The Concise Encyclopedia of Statistics. Springer...

Look up regression, regressions, or régression in Wiktionary, the free dictionary. Regression or regressions may refer to: Regression (film), a 2015 horror...

In statistics, the Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. A...

Theil–Sen estimator is a method for robustly fitting a line to sample points in the plane (a form of simple linear regression) by choosing the median of the...

by the presence of outliers . It is one of a number of methods for robust regression. Instead of the standard least squares method, which minimises the...

{\theta }}))} . P. Rousseeuw and V. Yohai, Robust Regression by Means of S-estimators, from the book: Robust and nonlinear time series analysis, pages...

regression relative to ordinary least squares regression is that the quantile regression estimates are more robust against outliers in the response measurements...

combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model...

cross-entropy loss for logistic regression is the same as the gradient of the squared-error loss for linear regression. That is, define X T = ( 1 x 11...

especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent...

context of linear regression and time series analysis. These are also known as heteroskedasticity-robust standard errors (or simply robust standard errors)...

In robust statistics, repeated median regression, also known as the repeated median estimator, is a robust linear regression algorithm. The estimator...

well-separated. Then M-estimation is consistent. Two-step M-estimator Robust statistics Robust regression Redescending M-estimator S-estimator Fréchet mean Hayashi...

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...

but should have a different regression line (a robust regression would have been called for). The calculated regression is offset by the one outlier...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than...

squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; instead of...

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

Ridge regression (also known as Tikhonov regularization, named for Andrey Tikhonov) is a method of estimating the coefficients of multiple-regression models...

Extreme value theory Influential observation Random sample consensus Robust regression Studentized residual Winsorizing Grubbs, F. E. (February 1969). "Procedures...

In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations...

Standardized covariance Standardized slope of the regression line Geometric mean of the two regression slopes Square root of the ratio of two variances...