ニューラルネットワークと線形回帰分析のハイブリッド解析法の実際 : 建築コスト価格モデルへの適用

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This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is presented and a hedonic building price model is used to further illustrate the modus operandi of the hybrid approach. Our analysis on building price is based on the data in Cheung and Skitmore (2006), and we find that the hybrid approach improves their model by a better structure of the input variables for the building prices.