Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm.
Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions th...
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0249604&type=printable |
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| author | Chénangnon Frédéric Tovissodé Aliou Diop Romain Glèlè Kakaï |
| author_facet | Chénangnon Frédéric Tovissodé Aliou Diop Romain Glèlè Kakaï |
| author_sort | Chénangnon Frédéric Tovissodé |
| collection | DOAJ |
| description | Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions thereby resulting into wrong and misleading decisions. This study brings forward an approach governed by skew generalized t distributions that belong to a class of potentially skewed and heavy tailed distributions. Interestingly, both the traditional logistic and probit mixed models, as well as other available methods can be utilized within the skew generalized t-link model (SGTLM) frame. We have taken advantage of the Expectation-Maximization algorithm accelerated via parameter-expansion for model fitting. We evaluated the performance of this approach to GLMMs through a simulation experiment by varying sample size and data distribution. Our findings indicated that the proposed methodology outperforms competing approaches in estimating population parameters and predicting random effects, when the traditional link and normality assumptions are violated. In addition, empirical standard errors and information criteria proved useful for detecting spurious skewness and avoiding complex models for probit data. An application with respiratory infection data points out to the superiority of the SGTLM which turns to be the most adequate model. In future, studies should focus on integrating the demonstrated flexibility in other generalized linear mixed models to enhance robust modeling. |
| format | Article |
| id | doaj-art-a9bc3df3e81d45bc833005b9626acfc7 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-a9bc3df3e81d45bc833005b9626acfc72025-08-20T02:00:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01164e024960410.1371/journal.pone.0249604Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm.Chénangnon Frédéric TovissodéAliou DiopRomain Glèlè KakaïBinary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions thereby resulting into wrong and misleading decisions. This study brings forward an approach governed by skew generalized t distributions that belong to a class of potentially skewed and heavy tailed distributions. Interestingly, both the traditional logistic and probit mixed models, as well as other available methods can be utilized within the skew generalized t-link model (SGTLM) frame. We have taken advantage of the Expectation-Maximization algorithm accelerated via parameter-expansion for model fitting. We evaluated the performance of this approach to GLMMs through a simulation experiment by varying sample size and data distribution. Our findings indicated that the proposed methodology outperforms competing approaches in estimating population parameters and predicting random effects, when the traditional link and normality assumptions are violated. In addition, empirical standard errors and information criteria proved useful for detecting spurious skewness and avoiding complex models for probit data. An application with respiratory infection data points out to the superiority of the SGTLM which turns to be the most adequate model. In future, studies should focus on integrating the demonstrated flexibility in other generalized linear mixed models to enhance robust modeling.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0249604&type=printable |
| spellingShingle | Chénangnon Frédéric Tovissodé Aliou Diop Romain Glèlè Kakaï Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm. PLoS ONE |
| title | Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm. |
| title_full | Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm. |
| title_fullStr | Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm. |
| title_full_unstemmed | Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm. |
| title_short | Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm. |
| title_sort | inference in skew generalized t link models for clustered binary outcome via a parameter expanded em algorithm |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0249604&type=printable |
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