| Autor: |
Xinlei Zuo, Juan Ding, Junjian Zhang, Wenjun Xiong |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Mathematics, Vol 12, Iss 5, p 686 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2227-7390 |
| DOI: |
10.3390/math12050686 |
| Popis: |
Group testing has been verified as a cost-effective and time-efficient approach, where the individual samples are pooled with a predefined group size for subsequent testing. Recent research has explored the integration of covariate information to improve the modeling of the group testing data. While existing works for high-dimensional data primarily focus on parametric models, this study considers a more flexible generalized nonparametric additive model. Nonlinear components are approximated using B-splines and model estimation under the sparsity assumption is derived employing group lasso. Theoretical results demonstrate that our method selects the true model with a high probability and provides consistent estimates. Numerical studies are conducted to illustrate the good performance of our proposed method, using both simulated and real data. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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