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最小假设下的非线性回归AI 解读

A curvilinear regression model is treated by linear programming methods, so as to require only mild restrictions on the nature of the non-linearities. Specifically a method is proposed that does not require assuming specific mathematical forms for the regression functions. Restrictive assumptions no stronger than monotonicity or concavity of the forms need be imposed. The linear programming formulations provide fits for the regression functions according to the criteria of…

用于回归分析的线性规划技术AI 解读

In regression problems alternative criteria of “best fit” to least squares are least absolute deviations and least maximum deviations. In this paper it is noted that linear programming techniques may be employed to solve the latter two problems. In particular, if the linear regression relation contains p parameters, minimizing the sum of the absolute value of the “vertical” deviations from the regression line is shown to reduce to a p equation linear programming model with…

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