In multiple regression, different techniques are available to deal with the situation where predictors large in number, and multicollinearity exists among them. Some of these approaches rely on correlation others depend principal components. To cope influential observations (outliers, leverage, or both) data matrix for regression purposes, two proposed this paper. These Robust Correlation Based...

The least squares linear regression estimator is well-known to be highly sensitive to unusual observations in the data, and as a result many more robust estimators have been proposed as alternatives. One of the earliest proposals was least-sum of absolute deviations (LAD) regression, where the regression coefficients are estimated through minimization of the sum of the absolute values of the re...

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...