JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (9): 56-63.doi: 10.6040/j.issn.1671-9352.0.2025.053

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Prediction of HIV infection rates with missing data: based on a functional data analysis approach

PENG Qianqian, LING Nengxiang*   

  1. School of Mathematics, Hefei University of Technology, Hefei 230009, Anhui, China
  • Published:2026-09-30

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Abstract: A partial functional linear quantile regression model is constructed to address the prediction of human immunodeficiency virus(HIV)infection rates. The model overcomes the limitations of traditional mean regression and captures the influence characteristics of different indicators on HIV infection rates. For the complex scenario where both functional explanatory variable and scalar response variable are missing in real-world data, a joint processing method integrating functional data reconstruction and regression imputation is proposed. Additionally, the proposed method is compared with other commonly used curve reconstruction and data imputation approaches. The results demonstrate that the proposed method exhibits advantages in handling missing data and improving prediction accuracy.

Key words: functional data, partial linear quantile regression model, HIV infection rate, missing data

CLC Number: 

  • O212
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