Open Access
2022 Localising change points in piecewise polynomials of general degrees
Yi Yu, Sabyasachi Chatterjee, Haotian Xu
Author Affiliations +
Electron. J. Statist. 16(1): 1855-1890 (2022). DOI: 10.1214/21-EJS1963


In this paper we are concerned with a sequence of univariate random variables with piecewise polynomial means and independent sub-Gaussian noise. The underlying polynomials are allowed to be of arbitrary but fixed degrees. All the other model parameters are allowed to vary depending on the sample size.

We propose a two-step estimation procedure based on the 0-penalisation and provide upper bounds on the localisation error. We complement these results by deriving global information-theoretic lower bounds, which show that our two-step estimators are nearly minimax rate-optimal. We also show that our estimator enjoys near optimally adaptive performance by attaining individual localisation errors depending on the level of smoothness at individual change points of the underlying signal. In addition, under a special smoothness constraint, we provide a minimax lower bound on the localisation errors. This lower bound is independent of the polynomial orders and is sharper than the global minimax lower bound.


The research of Yu and Xu are partially supported by EPSRC (EP/V013432/1).


Download Citation

Yi Yu. Sabyasachi Chatterjee. Haotian Xu. "Localising change points in piecewise polynomials of general degrees." Electron. J. Statist. 16 (1) 1855 - 1890, 2022.


Received: 1 June 2021; Published: 2022
First available in Project Euclid: 21 March 2022

MathSciNet: MR4396490
zbMATH: 07524965
Digital Object Identifier: 10.1214/21-EJS1963

Keywords: change point , minimax , piecewise polynomial

Vol.16 • No. 1 • 2022
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