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    <titleInfo>
        <title>Automated search algorithm for optimal generalized linear mixed models (GLMMs)</title>
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    <name type="personal">
        <namePart type="given">Miryeong</namePart>
        <namePart type="family">Koo</namePart>
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    <name type="personal">
        <namePart type="given">Jinming</namePart>
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            <title>Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers</title>
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            <namePart type="given">Joshua</namePart>
            <namePart type="family">Wilson</namePart>
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        <name type="personal">
            <namePart type="given">Christopher</namePart>
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        <name type="personal">
            <namePart type="given">Magdalen</namePart>
            <namePart type="family">Beiting Parrish</namePart>
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        <identifier type="isbn">979-8-218-84228-4</identifier>
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    <abstract>Only a limited number of predictors can be included in a generalized linear mixed model (GLMM) due to estimation algorithm divergence. This study aims to propose a machine learning based algorithm (e.g., random forest) that can consider all predictors without the convergence issue and automatically searches for the optimal GLMMs.</abstract>
    <identifier type="citekey">koo-zhang-2025-automated</identifier>
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        <url>https://aclanthology.org/2025.aimecon-main.38/</url>
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%0 Conference Proceedings
%T Automated search algorithm for optimal generalized linear mixed models (GLMMs)
%A Koo, Miryeong
%A Zhang, Jinming
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84228-4
%F koo-zhang-2025-automated
%X Only a limited number of predictors can be included in a generalized linear mixed model (GLMM) due to estimation algorithm divergence. This study aims to propose a machine learning based algorithm (e.g., random forest) that can consider all predictors without the convergence issue and automatically searches for the optimal GLMMs.
%U https://aclanthology.org/2025.aimecon-main.38/
%P 352-358

Markdown (Informal)

[Automated search algorithm for optimal generalized linear mixed models (GLMMs)](https://aclanthology.org/2025.aimecon-main.38/) (Koo & Zhang, AIME-Con 2025)

• Automated search algorithm for optimal generalized linear mixed models (GLMMs) (Koo & Zhang, AIME-Con 2025)

ACL

• Miryeong Koo and Jinming Zhang. 2025. Automated search algorithm for optimal generalized linear mixed models (GLMMs). In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 352–358, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).