首页|A censored quantile transformation model for Alzheimer's Disease data with multiple functional covariates

A censored quantile transformation model for Alzheimer's Disease data with multiple functional covariates

扫码查看
原文链接
  • NETL
  • NSTL
  • Oxford Univ Press
Alzheimer's disease (AD) is a progressive disease that starts from mild cognitive impairment and may eventually lead to irreversible memory loss. It is imperative to explore the risk factors associated with the conversion time to AD that is usually right-censored. Classical statistical models like mean regression and Cox models fail to quantify the impact of risk factors across different quantiles of a response distribution, and previous research has primarily focused on modelling a single functional covariate, possibly overlooking the interdependence among multiple functional covariates and other crucial features of the distribution. To address these issues, this paper proposes a multivariate functional censored quantile regression model based on dynamic power transformations, which relaxes the global linear assumption and provides more robustness and flexibility. Uniform consistency and weak convergence of the quantile process are established. Simulation studies suggest that the proposed method outperforms the existing approaches. Real data analysis shows the importance of both left and right hippocampal radial distance curves for predicting the conversion time to AD at different quantile levels.

ADNI studycensored quantile regressionmultivariate functional datatransformation model

Shaopei Ma、Man-lai Tang、Kerning Yu、Wolfgang Karl Hardle、Zhihao Wang、Wei Xiong、Xueliang Zhang、Kai Wang、Liping Zhang、Maozai Tian

展开 >

School of Statistics, University of International Business and Economics, Beijing, China

Centre of Data Innovation Research, Department of Physics, Astronomy and Mathematics, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK

Department of Mathematics, Brunei University London, Uxbridge, UK

Blockchain Research Center, Humboldt-Universitat zu Berlin, Berlin, Germany

Institute of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi, China

Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China

Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China||Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China

展开 >

2025

10.1093/jrsssa/qnae061

Journal of the Royal Statistical Society, Series A. Statistics in society

Journal of the Royal Statistical Society, Series A. Statistics in society

ISSN：0964-1998
年,卷(期)：2025.188(Pt.2)
  • 53