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Variable selection for quantile autoregressive model: Bayesian methods versus classical methods.

Bo Peng1, Kai Yang1, Xiaogang Dong1

  • 1School of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China.

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|April 17, 2024
PubMed
Summary
This summary is machine-generated.

This study presents Bayesian variable selection methods for quantile autoregressive models. These reliable methods effectively analyze relationships in datasets like Bike Sharing, using fast-converging Gibbs sampling algorithms.

Keywords:
Bayesian order shrinkageBayesian quantile autoregressionBayesian variable selectionexplanatory variablesspike-and-slab prior

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Area of Science:

  • Statistics
  • Econometrics
  • Data Science

Background:

  • Quantile autoregressive models are crucial for analyzing conditional quantiles.
  • Variable selection is essential for building parsimonious and interpretable models.
  • Bayesian methods offer a robust framework for statistical inference and model selection.

Purpose of the Study:

  • To introduce three novel Bayesian variable selection methods for quantile autoregressive models.
  • To develop and validate Gibbs sampling algorithms for these methods.
  • To assess the performance and applicability of the proposed methods using simulations and real-world data.

Main Methods:

  • Development of three Bayesian variable selection techniques.
  • Implementation of Gibbs sampling algorithms with diverse prior specifications.
  • Application to simulated data and the Bike Sharing dataset.

Main Results:

  • Gibbs sampling algorithms demonstrated fast convergence.
  • Bayesian variable selection methods proved reliable and feasible.
  • The methods accurately identified relevant explanatory variables in the Bike Sharing data.

Conclusions:

  • The proposed Bayesian variable selection methods are effective for quantile autoregressive models.
  • These methods are suitable for analyzing complex datasets, including time-series data like bike rentals.
  • The study confirms the practical utility and reliability of the developed techniques.