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

ABSTRACT: In this article, we introduce three Bayesian variable selection methods for the quantile autoregressive model with explanatory variables. The Gibbs sampling algorithms are developed for each method by setting different priors. The numerical simulations suggest that the Gibbs sampling algorithms converge fast and Bayesian variable selection methods are reliable. A real example is given to analysis the relationship between the count of total rental bikes and five explanatory variables. Both simulations and data example indicate that the proposed methods are feasible, reliable, and appropriate for analyzing the Bike Sharing data set.

SUBMITTER: Peng B

PROVIDER: S-EPMC11018091 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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

Peng Bo B Yang Kai K Dong Xiaogang X

Journal of applied statistics 20230214 6

In this article, we introduce three Bayesian variable selection methods for the quantile autoregressive model with explanatory variables. The Gibbs sampling algorithms are developed for each method by setting different priors. The numerical simulations suggest that the Gibbs sampling algorithms converge fast and Bayesian variable selection methods are reliable. A real example is given to analysis the relationship between the count of total rental bikes and five explanatory variables. Both simula ...[more]

PMID: 38628448

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