Single-index logistic model for high-dimensional group testing data
Group testing is an efficient screening method that reduces the number of tests by pooling multiple samples, making it especially effective in low-prevalence settings. This strategy gained significant attention during the COVID-19 pandemic, and has since been applied to detect various infectious dis...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-02-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2025163 |
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| author | Changfu Yang Wenxin Zhou Wenjun Xiong Junjian Zhang Juan Ding |
| author_facet | Changfu Yang Wenxin Zhou Wenjun Xiong Junjian Zhang Juan Ding |
| author_sort | Changfu Yang |
| collection | DOAJ |
| description | Group testing is an efficient screening method that reduces the number of tests by pooling multiple samples, making it especially effective in low-prevalence settings. This strategy gained significant attention during the COVID-19 pandemic, and has since been applied to detect various infectious diseases, including HIV, chlamydia, gonorrhea, influenza, and Zika virus. In this paper, we introduce a semi-parametric logistic single-index model for analyzing high-dimensional group testing data, which is particularly flexible in capturing complex nonlinear relationships. The proposed method achieves variable selection by parameter regularization, which proves especially beneficial for extracting relevant information from high-dimensional data. The performance of the model is evaluated through simulations across four group testing strategies: master pool testing, Dorfman testing, halving testing, and array testing. Further validation is provided using real-world data. The results demonstrate that our approach offers a flexible and robust tool for analyzing high-dimensional group testing data, with important applications in epidemiology and public health. |
| format | Article |
| id | doaj-art-68d3b6aa8afe4520a00527902136b6b7 |
| institution | DOAJ |
| issn | 2473-6988 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-68d3b6aa8afe4520a00527902136b6b72025-08-20T03:17:09ZengAIMS PressAIMS Mathematics2473-69882025-02-011023523356010.3934/math.2025163Single-index logistic model for high-dimensional group testing dataChangfu Yang0Wenxin Zhou1Wenjun Xiong2Junjian Zhang3Juan Ding4School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, ChinaSchool of Mathematics, Hohai University, Nanjing 210098, ChinaSchool of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, ChinaSchool of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, ChinaSchool of Mathematics, Hohai University, Nanjing 210098, ChinaGroup testing is an efficient screening method that reduces the number of tests by pooling multiple samples, making it especially effective in low-prevalence settings. This strategy gained significant attention during the COVID-19 pandemic, and has since been applied to detect various infectious diseases, including HIV, chlamydia, gonorrhea, influenza, and Zika virus. In this paper, we introduce a semi-parametric logistic single-index model for analyzing high-dimensional group testing data, which is particularly flexible in capturing complex nonlinear relationships. The proposed method achieves variable selection by parameter regularization, which proves especially beneficial for extracting relevant information from high-dimensional data. The performance of the model is evaluated through simulations across four group testing strategies: master pool testing, Dorfman testing, halving testing, and array testing. Further validation is provided using real-world data. The results demonstrate that our approach offers a flexible and robust tool for analyzing high-dimensional group testing data, with important applications in epidemiology and public health.https://www.aimspress.com/article/doi/10.3934/math.2025163group testinglatent variablesingle-index modelhigh-dimensional datavariable selectionem algorithm |
| spellingShingle | Changfu Yang Wenxin Zhou Wenjun Xiong Junjian Zhang Juan Ding Single-index logistic model for high-dimensional group testing data AIMS Mathematics group testing latent variable single-index model high-dimensional data variable selection em algorithm |
| title | Single-index logistic model for high-dimensional group testing data |
| title_full | Single-index logistic model for high-dimensional group testing data |
| title_fullStr | Single-index logistic model for high-dimensional group testing data |
| title_full_unstemmed | Single-index logistic model for high-dimensional group testing data |
| title_short | Single-index logistic model for high-dimensional group testing data |
| title_sort | single index logistic model for high dimensional group testing data |
| topic | group testing latent variable single-index model high-dimensional data variable selection em algorithm |
| url | https://www.aimspress.com/article/doi/10.3934/math.2025163 |
| work_keys_str_mv | AT changfuyang singleindexlogisticmodelforhighdimensionalgrouptestingdata AT wenxinzhou singleindexlogisticmodelforhighdimensionalgrouptestingdata AT wenjunxiong singleindexlogisticmodelforhighdimensionalgrouptestingdata AT junjianzhang singleindexlogisticmodelforhighdimensionalgrouptestingdata AT juanding singleindexlogisticmodelforhighdimensionalgrouptestingdata |