Abstract
There are many research works and methods about change point detection in the literature. However, there are only a few that provide inference for such change points after being estimated. This work mainly focuses on a statistical analysis of change points estimated by the PRUTF algorithm, which incorporates trend filtering to determine change points in piecewise polynomial signals. This paper develops a methodology to perform statistical inference, such as computing p-values and constructing confidence intervals in the newly developed post-selection inference framework. Our work concerns both cases of known and unknown error variance. As pointed out in the post-selection inference literature, the length of such confidence intervals are undesirably long. To resolve this shortcoming, we also provide two novel strategies, global post-detection and local post-detection which are based on the intrinsic properties of change points. We run our proposed methods on real as well as simulated data to evaluate their performances.
Reza Mehrzi
Data Scientist
My research interests include building and developing predictive models using machine learning methods, deep learning object detection and semantic image segmentation, experimental designs, and survival analysis.