A computationally efficient model selection in the generalized linear mixed model |
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Authors: | Takuma Yoshida Masaru Kanba Kanta Naito |
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Institution: | 1. Graduate school of Science and Engineering, Shimane University, Matsue, 690-8504, Japan 2. Department of Mathematics, Shimane University, Matsue, 690-8504, Japan
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Abstract: | This paper is concerned with model selection in spline-based generalized linear mixed model. Exploiting the fact that smoothing
parameters can be expressed as the reciprocal ratio of the variances of random effect under the setting of estimation by regularization,
we propose a computationally efficient model selection procedure. Applications to some real data sets reveal that the proposed
method selects reasonable models and is very fast to implement. |
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Keywords: | |
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