Nonparametric Additive Regression for High-Dimensional Group Testing Data

Author:

Zuo Xinlei1,Ding Juan2,Zhang Junjian1,Xiong Wenjun1

Affiliation:

1. School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, China

2. School of Mathematics, Hohai University, Nanjing 210098, China

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.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Guangxi Natural Science Foundation

Publisher

MDPI AG

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