Inference for Change Points in High Dimensional Mean Shift Models

TL;DR

This paper develops optimal methods for estimating and constructing confidence intervals for change points in high-dimensional mean shift models, accommodating many change points, small jumps, and subexponential errors.

Contribution

It introduces a locally refitted least squares estimator with the sharpest simultaneous rate, enabling valid confidence intervals in high-dimensional settings.

Findings

Simultaneous estimation rate improved by a factor of log p

Component-wise distributions characterized under different jump regimes

Asymptotic independence of change point estimates established

Abstract

We consider the problem of constructing confidence intervals for the locations of change points in a high-dimensional mean shift model. To that end, we develop a locally refitted least squares estimator and obtain component-wise and simultaneous rates of estimation of the underlying change points. The simultaneous rate is the sharpest available in the literature by at least a factor of $\log p,$ while the component-wise one is optimal. These results enable existence of limiting distributions. Component-wise distributions are characterized under both vanishing and non-vanishing jump size regimes, while joint distributions for any finite subset of change point estimates are characterized under the latter regime, which also yields asymptotic independence of these estimates. The combined results are used to construct asymptotically valid component-wise and simultaneous confidence intervals for the change point parameters. The results are established under a high dimensional scaling, allowing for diminishing jump sizes, in the presence of diverging number of change points and under subexponential errors. They are illustrated on synthetic data and on sensor measurements from smartphones for activity recognition.