Bias Robust Estimation in Orthogonal Regression

Robust L1 orthogonal regression

Assessing the linear relationship between a set of continuous predictors and a continuous response is a well studied problem in statistics and is applied in many data mining situations. L2 based methods such as ordinary least squares and principal components regression can be used to determine this relationship. However, both of these methods become impaired when multicollinearity is present. T...

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Min-max Bias Robust Regression

This the problem maximum asymptotic bias of regression estimates over a-contamination for the joint of the response carriers. Two classes of estimates are treated: (1) Msestimates with bounded function p applied to the scaled residuals, using a very general class of scale estimates, and (2) Bounded influence function type generalized M-estimates. Estimates in the first class are oblta1rled as p...

Bias Robust Estimation of Scale

Efficiency Constrained Bias Robust Estimation of Location