Abstract
We propose a likelihood-based variable selection method for selecting explanatory variables in normality-assumed multivariate linear regression contexts. The proposed method is reasonably fast in terms of run-time, and it has a selection consistency when the sample size always tends to infinity, but the number of response and explanatory variables does not necessarily have to tend to infinity. It can be expected that the probability of selecting the true subset by the proposed method is high under a moderate sample size.
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Acknowledgements
This work was supported by funding from JSPS KAKENHI (grant numbers JP20K14363, JP20H04151, and JP19K21672 to Ryoya Oda; and JP16H03606, JP18K03415, and JP20H04151 to Hirokazu Yanagihara).
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Oda, R., Yanagihara, H. (2021). A Consistent Likelihood-Based Variable Selection Method in Normal Multivariate Linear Regression. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_33
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DOI: https://doi.org/10.1007/978-981-16-2765-1_33
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