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Related Experiment Video

Updated: Jul 15, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models.

Xitong Liang1, Samuel Livingstone1, Jim Griffin1

  • 1Department of Statistical Science, University College London, London WC1E 6BT, UK.

Entropy (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient computational scheme for high-dimensional Bayesian variable selection in generalized linear and survival models. The novel PARNI proposal improves model sampling and marginal likelihood estimation, outperforming existing methods in simulations and genetic mapping data.

Keywords:
Bayesian computationBayesian variable selectionadaptive Markov Chain Monte Carlogeneralised linear modelsspike-and-slab priorssurvival models

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Area of Science:

  • Computational Statistics
  • Statistical Modeling
  • Bioinformatics

Background:

  • High-dimensional Bayesian variable selection in generalized linear and survival models is computationally challenging.
  • Existing methods like Reversible Jump Markov Chain Monte Carlo (RJMCMC) and data augmentation have implementation difficulties or limitations.
  • Estimating marginal likelihood using Laplace approximation or pseudo-marginal methods can be computationally expensive.

Purpose of the Study:

  • To develop an efficient computational scheme for high-dimensional Bayesian variable selection.
  • To introduce a novel proposal for sampling models directly from marginal posterior distributions.
  • To present an accurate and efficient method for marginal likelihood estimation.

Main Methods:

  • Extended Point-wise implementation of Adaptive Random Neighbourhood Informed (PARNI) proposal for efficient model sampling.
  • An adaptive parameter-based estimation method for marginal likelihood, building on approximate Laplace approximation.
  • A new method for adapting algorithmic tuning parameters of the PARNI proposal using a combination of warm-start and ergodic average estimates.

Main Results:

  • The novel PARNI proposal efficiently samples models directly from marginal posterior distributions.
  • The proposed marginal likelihood estimation method is accurate and efficient.
  • The adaptive tuning method improves the performance of the PARNI proposal.
  • Numerical results demonstrate the superior efficiency of PARNI compared to the add-delete-swap proposal.

Conclusions:

  • The developed PARNI proposal offers an efficient solution for high-dimensional Bayesian variable selection in generalized linear and survival models.
  • The new marginal likelihood estimation and parameter adaptation methods enhance computational efficiency and accuracy.
  • The approach shows significant promise, validated by simulations and real-world genetic mapping data analysis.