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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
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Empirical Likelihood for Censored Linear Regression and Variable Selection.

Tong Tong Wu1, Gang Li2, Chengyong Tang3

  • 1Department of Biostatistics and Computational Biology, University of Rochester.

Scandinavian Journal of Statistics, Theory and Applications
|May 18, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel empirical likelihood approach for linear regression with right-censored data, enhancing survival analysis. The method offers robust statistical inferences and effective variable selection for complex datasets.

Keywords:
Accelerated failure time modelCoordinate descent algorithmHigh-dimensional data analysisLinear regression modelOracle propertyVariable selectionWilks’ theorem

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Nonparametric Statistics

Background:

  • The accelerated failure time (AFT) model offers an alternative to the Cox proportional hazards model for right-censored data.
  • Empirical likelihood (EL) provides a robust, nonparametric approach for statistical inference.
  • Conventional EL methods face challenges with dependent estimating equations in AFT models.

Purpose of the Study:

  • To develop a new empirical likelihood approach for linear regression models with right-censored data.
  • To address limitations of existing methods in handling dependent estimating equations.
  • To enable robust statistical inference and variable selection in survival analysis.

Main Methods:

  • A novel estimating equation is proposed for the AFT model.
  • A nested coordinate algorithm with majorization is employed for optimization.
  • Penalized empirical likelihood with quadratic approximation is used for variable selection.

Main Results:

  • The Wilks theorem is shown to hold for the new empirical likelihood.
  • Oracle properties are proven for penalized empirical likelihood variable selection.
  • The method demonstrates effectiveness on simulated data and a SEER small intestine cancer dataset.

Conclusions:

  • The proposed empirical likelihood approach provides a robust and effective method for linear regression with right-censored data.
  • This method extends the benefits of empirical likelihood to AFT models, improving survival data analysis.
  • The approach facilitates reliable variable selection, even with a large number of predictors.