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2014•Journal of Kunming University of Science and TechnologyRequires access

Solution and Application of Linear Programming in Multivariate Linear Regression

Zhang Ai-la

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Abstract

In the multivariate linear regression model,the least square method is a commonly used method to estimate the parameters of the regression equation. Total sum of square criterion and the most greatly absolute dispersion minimum criterion are the two substitution criteria for the least square method. An objective function is established by using linear programming to convert the model to a linear programming model. The obtained parameters of the model are then used for forecast. The results of a case study indicates that the linear programming model eliminates the unusual statistical data influence on the regression equation effectively and has a good forecast effect.

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What this paper is about

In the multivariate linear regression model,the least square method is a commonly used method to estimate the parameters of the regression equation. Total sum of square criterion and the most greatly absolute dispersion minimum criterion are the two substitution criteria for the least square method. An objective function is established by using linear programming to convert the model to a linear programming model. The obtained parameters of the model are then used for forecast. The results of a case study indicates that the linear programming model eliminates the unusual statistical data influence on the regression equation effectively and has a good forecast effect.

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Available abstract

In the multivariate linear regression model,the least square method is a commonly used method to estimate the parameters of the regression equation. Total sum of square criterion and the most greatly absolute dispersion minimum criterion are the two substitution criteria for the least square method. An objective function is established by using linear programming to convert the model to a linear programming model. The obtained parameters of the model are then used for forecast. The results of a case study indicates that the linear programming model eliminates the unusual statistical data influence on the regression equation effectively and has a good forecast effect.

Key concepts: Proper linear model, Bayesian multivariate linear regression, Linear predictor function, Linear regression, Multivariate statistics, Mathematics, Linear programming, General linear model

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