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

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 pr...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 49; no. 12; pp. 3380 - 3395
Main Authors Wang, Leyang, Sun, Jianqiang
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 01.12.2020
Taylor & Francis Ltd
Subjects
coefficient matrix
Dependent variables
Independent variables
Iterative methods
Iterative solution
Partial EIV model
Regression models
regression prediction
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Summary: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.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2018.1547399