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A computationally efficient model selection in the generalized linear mixed model

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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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Authors and Affiliations

  1. Graduate school of Science and Engineering, Shimane University, Matsue, 690-8504, Japan

    Takuma Yoshida & Masaru Kanba

  2. Department of Mathematics, Shimane University, Matsue, 690-8504, Japan

    Kanta Naito

Authors
  1. Takuma Yoshida
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  2. Masaru Kanba
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  3. Kanta Naito
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Correspondence to Kanta Naito.

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Yoshida, T., Kanba, M. & Naito, K. A computationally efficient model selection in the generalized linear mixed model. Comput Stat 25, 463–484 (2010). https://doi.org/10.1007/s00180-010-0187-3

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  • DOI: https://doi.org/10.1007/s00180-010-0187-3

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