A new Bayesian effect selection method for additive quantile regression separates linear and nonlinear components via Demmler-Reinsch basis and spike-slab priors, demonstrated on NO2 pollution data.
and Bondell, Howard D
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Bayesian Effect Selection for Additive Quantile Regression with an Application to Air Pollution Thresholds
A new Bayesian effect selection method for additive quantile regression separates linear and nonlinear components via Demmler-Reinsch basis and spike-slab priors, demonstrated on NO2 pollution data.