Subgroup detection based on partially linear additive individualized model with missing data in response

Tingting Cai, Jianbo Li, Qin Zhou, Songlou Yin and Riquan Zhang

Computational Statistics & Data Analysis, 2024, vol. 192, issue C

Abstract: Based on partially linear additive individualized model, a fusion-penalized inverse probability weighted least squares method is proposed to detect the subgroup for missing data in response. Firstly, the B-spline technique is used to approximate the unknown additive individualized functions and then an inverse probability weighted quadratic loss function is established with fusion penalty on the difference of subject-wise B-spline coefficients. Secondly, minimization of such quadratic loss function leads to the estimation of linear regression parameters and individualized B spline coefficients. With a proper tuning parameter, some differences in penalty term are shrunk into zero and thus the corresponding subjects will be clustered into the same subgroup. Thirdly, a clustering method is developed to automatically determine the subgroup membership for the subjects with missing data. Fourthly, large sample properties of resulting estimates are given under some regular conditions. Finally, numerical studies are presented to illustrate the performance of the proposed subgroup detection method.

Keywords: Missing data; Subgroup detection; ADMM; B-spline; Inverse probability weighted method; Partially linear additive individualized model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:192:y:2024:i:c:s0167947323002219

DOI: 10.1016/j.csda.2023.107910

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