Abstract: The regression model was traditionally established by using the least squares (LS) method where the errors of independent variables were ignored. Although the weighted total least squares (TLS) method that captures errors of both dependent and independent variables was extensively studied for regression analysis in recent years, it still neglects the errors of independent variables when predicting the corresponding dependent variables.This paper puts forward a seamless linear regression and prediction model which estimates regression parameters and predicts dependent variables simultaneously by considering the errors of all variables.In the seamless model, the errors of independent variables in the prediction model are predicted and corrected to improve the prediction accuracy.The several existing regression models are theoretically proved to be the special cases of the proposed seamless model. The experimental results show that the proposed seamless model outperforms the other existing models in the sense of prediction accuracy, especially when the error correlation of variables is significant.

Key words: seamless linear regression model, model prediction, observation error estimation, error correlation