Robust Procedure for Change-Point Estimation Using Quantile Regression Model with Asymmetric Laplace Distribution

The usual mean change-point detecting method based on normal linear regression is not robust to heavy-tailed data with potential outlying points. We propose a estimation procedure quantile model asymmetric Laplace error distribution and develop non-iterative sampling algorithm from Bayesian perspective. can generate independently identically distributed samples approximately the posterior of position change-point, which be used for statistical inferences straightforwardly. combines robustness computational efficiency algorithm. A simulation study conducted illustrate performance satisfying findings, finally, real analyzed show usefulness by comparison detection regression.