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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Variable selection in semi-parametric models.

Hongmei Zhang1, Arnab Maity2, Hasan Arshad3

  • 1Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, TN, USA hzhang@sc.edu.

Statistical Methods in Medical Research
|August 31, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian variable selection for semi-parametric models, efficiently identifying important linear or non-linear effects. The methods were successfully applied to identify methylation sites linked to smoking exposure.

Keywords:
Bayesian methodsGaussian kernelnon-linear effectspartially linear regressionprobit regressionreproducing kernelvariable selection

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Area of Science:

  • Statistics
  • Genetics
  • Bioinformatics

Background:

  • Semi-parametric regression models are crucial for analyzing complex biological data.
  • Variable selection is essential for identifying key factors in high-dimensional datasets.
  • Bayesian methods offer a robust framework for statistical inference and model selection.

Purpose of the Study:

  • To develop and evaluate Bayesian variable selection methods for semi-parametric regression.
  • To identify significant variables, including those with non-linear joint effects.
  • To apply these methods to identify epigenetic markers associated with environmental exposures.

Main Methods:

  • Utilizing reproducing kernels to model non-linear variable interactions.
  • Introducing indicator variables within kernels for variable inclusion/exclusion.
  • Employing posterior probabilities for variable selection in Gaussian and probit models.
  • Conducting simulations to validate method performance.

Main Results:

  • The proposed Bayesian methods efficiently select relevant variables, irrespective of effect linearity.
  • Simulations demonstrate high accuracy in identifying true effects.
  • Application to real data successfully identified cytosine phosphate guanine methylation sites associated with maternal smoking and cotinine levels.

Conclusions:

  • The developed Bayesian variable selection approach is effective for semi-parametric models.
  • These methods can uncover complex, non-linear relationships in biological data.
  • Identified methylation sites offer insights into smoking's health impacts and aid medical research.