The article discusses new methods for estimating parameters in a regression model when there are strong relationships between variables (multicollinearity) and unusual data points (outliers). The researchers found that traditional methods like Principal Component Regression and Ridge Regression can struggle with outliers. They introduced new techniques called RWPCLTS and RWPCLMS, which showed better performance in handling both multicollinearity and outliers. These methods were tested on cigarette data and through simulations, showing that they are effective in dealing with these challenges.