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Flexible link functions in nonparametric binary regression with Gaussian process priors.

Dan Li1, Xia Wang2, Lizhen Lin3

  • 1Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio 45221, U.S.A.

Biometrics
|December 22, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible binary regression model using a generalized extreme value link function and Gaussian processes. The new model improves prediction accuracy for binary outcomes compared to existing methods.

Keywords:
FlexibilityGaussian processGeneralized extreme value distributionLatent variableMarkov chain Monte CarloPosterior consistency

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Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Binary response data is common in various scientific fields.
  • Gaussian processes are used for nonlinear system modeling, including binary regression.
  • Existing link functions (probit, logit) lack flexibility in skewness.

Purpose of the Study:

  • To propose a flexible binary regression model.
  • To address the limitations of fixed skewness in common link functions.
  • To improve prediction of binary outcomes.

Main Methods:

  • Combined a generalized extreme value link function with a Gaussian process prior.
  • Employed Bayesian computation for model estimation.
  • Demonstrated posterior consistency.

Main Results:

  • The proposed model shows flexibility and improved performance.
  • Outperformed alternative models in simulation studies and real data examples.
  • The generalized extreme value link function adapts skewness based on data.

Conclusions:

  • The novel flexible binary regression model offers enhanced predictive capabilities.
  • The model's adaptability in link function skewness is a key advantage.
  • This approach provides a valuable tool for analyzing binary response data.