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Bayesian monotone single-index quantile regression model with bounded response and misaligned functional covariates.

Shengxian Ding1, Debajyoti Sinha2, Greg Hajcak3

  • 1Department of Biostatistics, Yale University, Connecticut 06510, United States.

Biometrics
|October 29, 2025
PubMed
Summary

New Bayesian methods predict adolescent depression risk by analyzing parental history and neural reward responses. This approach offers a clinically interpretable index for better mental health research.

Keywords:
asymmetric laplace distributiondysphoriafunction registrationmonotone linkquantile regressionsingle-index

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Biostatistics

Background:

  • Adolescent depression is linked to parental depression history and behavioral factors.
  • Existing research often uses linear regression, limiting the analysis of complex neural data.
  • Neural responses to rewards are crucial but challenging to integrate with other risk factors.

Purpose of the Study:

  • To develop a novel statistical framework for predicting future adolescent depression.
  • To integrate scalar risk factors (e.g., parental depression) with functional neural data (reward processing).
  • To overcome limitations of traditional regression models in capturing complex relationships.

Main Methods:

  • Proposed a Bayesian quantile regression framework.
  • Developed a single-index summary for scalar and functional covariates.
  • Incorporated a monotone link function for nonlinear relationships and interactions.
  • Jointly analyzed functional covariates and their registration within the quantile regression.

Main Results:

  • The novel Bayesian method outperforms existing single-index models, especially with mixed covariate types.
  • Simulation studies validate the framework's accuracy and robustness.
  • The approach yields a statistically principled summary of neural reward processing relevant to depression risk.

Conclusions:

  • The proposed Bayesian quantile regression framework offers a powerful tool for understanding adolescent depression.
  • This method provides a clinically interpretable index for future depression risk assessment.
  • The study highlights the importance of integrating neurobiological and clinical data for mental health research.