係数成分制約を用いた回帰分析法の提案と北海道の稲作生産性に対する気温の影響分析

概要

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In regression analysis a traditional approach, the so-called constrained coefficient approach, is usually used for model selection. We propose a new approach, named as constrained coefficient-component approach, instead. The constrained coefficient-component approach is developed based on a basic model which contains all of the explanatory variables that may be available and will be meaningful for practical applications. The procedure for the constrained coefficient-component approach is as follows. Firstly, we define a vector of coefficient-components by using an orthogonal transformation of the vector of regression coefficients in the basic model. Then, we construct a number of contending models by constraining some coefficient-components to be zeros. Further, the best model among all the contending models is selected by using Akaike information criterion, AIC. The constrained coefficient-component approach has many advantages, e.g., (1) the total number of the contending models can be reduced so that the process of model selection becomes really easy; (2) by using the newly-proposed approach we can obtain more stable estimation for parameters and a model that has better performance measuring by AIC. The constrained coefficient-component approach is applied to analyzing the influence of temperature on productivity of rice production in Hokkaido as an example.
旭川大学の論文
2005-06-30