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Bayesian Estimation of Partial Functional Tobit Censored Quantile Regression Model.

Chunjie Wang1, Zhexin Lu1, Chuchu Wang2

  • 1School of Mathematics and Statistics, Changchun University of Technology, Changchun, China.

Statistics in Medicine
|June 10, 2025
PubMed
Summary

This study introduces a novel statistical model to analyze laryngeal cancer risk factors using imaging and clinical data. The findings reveal specific laryngeal regions associated with disease progression, offering insights for early diagnosis.

Keywords:
MCMC methodTobit censored quantile regressionfunctional data analysispartial functional linear regression model

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Area of Science:

  • Medical imaging analysis
  • Statistical modeling
  • Oncology

Background:

  • Imaging data is crucial for disease diagnosis, revealing links between features and conditions.
  • Understanding laryngeal cancer risk factors requires advanced analytical methods.

Purpose of the Study:

  • To propose a partial functional Tobit censored quantile regression (PFTCQR) model.
  • To investigate quantile-specific relationships between laryngeal cancer incidence and predictors.
  • To enhance statistical modeling for disease analysis.

Main Methods:

  • Utilized functional principal component analysis and moment method for functional predictor estimation.
  • Developed a Markov chain Monte Carlo (MCMC) algorithm with asymmetric Laplace distribution (ALD).
  • Extended the PFTCQR model to composite quantile regression with variable selection.

Main Results:

  • The PFTCQR model effectively estimated relationships between imaging/clinical data and laryngeal cancer.
  • Simulation studies and real data application demonstrated the method's robustness.
  • Identified specific laryngeal regions significantly associated with disease progression.

Conclusions:

  • The proposed PFTCQR model offers valuable insights into laryngeal cancer risk factors.
  • The method enhances parameter estimation and model fitting for complex medical data.
  • Findings contribute to understanding disease progression and potential early detection strategies.