Change-point estimation under adaptive sampling

TL;DR

This paper introduces an adaptive sampling method for efficiently estimating change-points in regression models, improving convergence rates and providing practical guidelines for implementation.

Contribution

It proposes a multistage adaptive procedure that accelerates change-point estimation and derives its asymptotic distribution for practical tuning.

Findings

Accelerates convergence rate of change-point estimates.

Provides asymptotic distribution for the estimator.

Demonstrates improved efficiency with real and synthetic data.

Abstract

We consider the problem of locating a jump discontinuity (change-point) in a smooth parametric regression model with a bounded covariate. It is assumed that one can sample the covariate at different values and measure the corresponding responses. Budget constraints dictate that a total of $n$ such measurements can be obtained. A multistage adaptive procedure is proposed, where at each stage an estimate of the change point is obtained and new points are sampled from its appropriately chosen neighborhood. It is shown that such procedures accelerate the rate of convergence of the least squares estimate of the change-point. Further, the asymptotic distribution of the estimate is derived using empirical processes techniques. The latter result provides guidelines on how to choose the tuning parameters of the multistage procedure in practice. The improved efficiency of the procedure is demonstrated using real and synthetic data. This problem is primarily motivated by applications in engineering systems.