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- W4378945515 abstract "Quantile regression continues to increase in usage, providing a useful alternative to customary mean regression. Primary implementation takes the form of so-called multiple quantile regression, creating a separate regression for each quantile of interest. However, recently, advances have been made in joint quantile regression, supplying a quantile function which avoids crossing of the regression across quantiles. Here, we turn to quantile autoregression (QAR), offering a fully Bayesian version. We extend the initial quantile regression work of Koenker and Xiao (2006) in the spirit of Tokdar and Kadane (2012). We offer a directly interpretable parametric model specification for QAR. Further, we offer a p-th order QAR(p) version, a multivariate QAR(1) version, and a spatial QAR(1) version. We illustrate with simulation as well as a temperature dataset collected in Arag'on, Spain." @default.
- W4378945515 created "2023-06-01" @default.
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- W4378945515 date "2023-05-30" @default.
- W4378945515 modified "2023-09-29" @default.
- W4378945515 title "Bayesian joint quantile autoregression" @default.
- W4378945515 doi "https://doi.org/10.48550/arxiv.2305.19080" @default.
- W4378945515 hasPublicationYear "2023" @default.
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