Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle
基于最小描述长度原则的分段分位数回归模型选择
This article proposes new model-fitting techniques for quantiles of an observed data sequence, including methods for data segmentation and variable selection. The main contribution, however, is in providing a means to perform these two tasks simultaneously. This is achieved by matching the data with the best-fitting piecewise quantile regression model, where the fit is determined by a penalization derived from the minimum description length principle. The resulting…