Nonparametric Additive Regression for High-Dimensional Group Testing Data

Xinlei Zuo, Juan Ding (), Junjian Zhang and Wenjun Xiong ()
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Xinlei Zuo: School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, China
Juan Ding: School of Mathematics, Hohai University, Nanjing 210098, China
Junjian Zhang: School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, China
Wenjun Xiong: School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, China

Mathematics, 2024, vol. 12, issue 5, 1-21

Abstract: 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.

Keywords: group testing; nonparametric regression; variable selection; measurement error (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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