Nonparametric Additive Regression for High-Dimensional Group Testing Data
- Resource Type
- article
- Authors
- Xinlei Zuo; Juan Ding; Junjian Zhang; Wenjun Xiong
- Source
- Mathematics, Vol 12, Iss 5, p 686 (2024)
- Subject
- group testing
nonparametric regression
variable selection
measurement error
Mathematics
QA1-939
- Language
- English
- ISSN
- 2227-7390
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.