| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Outline of Final Research Achievements |
We proposed a fast and simple consistent variable selection method in multivariate linear regression models when the numbers of response and explanatory variables are large. The decision whether an explanatory variable is necessary or not is based on the difference between the model selection criteria of the full model and a candidate model in which only the target explanatory variable is removed. The consistency of the model selection criterion used was evaluated using asymptotic theory, in which the sample size goes to infinity under the condition that the sum of the numbers of response and explanatory variables divided by the sample size converges to a constant less than 1. By guaranteeing the consistency with this asymptotic theory, we could propose a variable selection method that is expected to increase the probability of selecting the true explanatory variables regardless of the sizes of the numbers of response and explanatory variables if the sample size is large enough.
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