Ryuta Tamura

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This paper proposes a method for eliminating multicollinearity from linear regression models. Specifically, we select the best subset of explanatory variables subject to the upper bound on the condition number of the correlation matrix of selected variables. We first develop a cutting plane algorithm that, to approximate the condition number constraint,(More)
The variance inflation factor, VIF, is the most frequently used indicator for detecting multicollinearity in multiple linear regression models. This paper proposes two mixed integer quadratic optimization formulations for selecting the best subset of explanatory variables under upper-bound constraints on VIF of selected variables. Computational results(More)
We developed a system to extract Japanese anime-related words, i.e., Japanese NEs (named entities) in the anime-related domain. Since the NEs in the area, such as the titles of anime or the names of characters, were domain-specific, we started by building a tagged corpus and then used it for the experiments. We examined to see if the existing corpora were(More)
The variance inflation factor, VIF, is frequently used for detecting multicollinearity in multiple linear regression models. This paper proposes a mixed integer quadratic optimization formulation for selecting the best subset of explanatory variables under the upper bound constraints on the VIF of selected variables.
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