Regression prediction method that is based on the partial errors-in-variables model

Abstract

The observation errors of independent variables in model prediction are typically neglected in traditional regression models, which leads to the decreased prediction accuracy. In this paper, a new regression prediction method that is based on the partial errors-in-variables (partial EIV) model is proposed, which takes into account the observation errors of all variables. It observes that each element of the coefficient matrix is an expression or a function. The partial EIV model is transformed into a linear model and constructed in the form of indirect adjustment for iterative solution. By considering the errors in the independent variables when predicting the corresponding dependent variables with the partial EIV model, the proposed method can achieve higher prediction accuracy.

Keywords:

• Partial EIV model
• regression prediction
• coefficient matrix

Acknowledgments

The authors are grateful to the anonymous reviewers and Prof. N. Balakrishnan for their valuable comments, which improve the quality of this paper. This research is supported by the National Natural Science Foundation of China, Nos. 41874001 and 41664001; Support Program for Outstanding Youth Talents in Jiangxi Province, No. 20162BCB23050; and National Key Research and Development Program, No. 2016YFB0501405.