# Nonparametric Change Point Detection in Regression

## TL;DR

This paper introduces a new nonparametric change-point detection method for regression that is fully data-driven, tuning-free, and effective in both theoretical and practical scenarios, including financial data analysis.

## Contribution

It proposes a novel, tuning-free, data-driven change-point detection procedure for regression with proven theoretical guarantees and practical effectiveness.

## Key findings

- Proper control of type I error rate under null hypothesis
- High power approaching 1 under alternative hypothesis
- Successful detection of change-points in financial data

## Abstract

This paper considers the prominent problem of change-point detection in regression. The study suggests a novel testing procedure featuring a fully data-driven calibration scheme. The method is essentially a black box, requiring no tuning from the practitioner. The approach is investigated from both theoretical and practical points of view. The theoretical study demonstrates proper control of first-type error rate under $H_0$ and power approaching $1$ under $H_1$. The experiments conducted on synthetic data fully support the theoretical claims. In conclusion, the method is applied to financial data, where it detects sensible change-points. Techniques for change-point localization are also suggested and investigated.