Efficient Multiple Change Point Detection and Localization For High-Dimensional Quantile Regression with Heteroscedasticity
异方差高维分位数回归的多重变点检测与定位
Data heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this article, we consider potential structural changes and complicated tail distributions. There are various existing methods proposed to handle either structural changes or heteroscedasticity. However, it is difficult to handle them simultaneously. To overcome this limitation, we consider statistically and computationally efficient…