On change point detection in regression function using nonparametric autoregressive processes

Ben Célestin Kouassi (), Ouagnina Hili () and Edoh Katchekpele ()
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Ben Célestin Kouassi: Institut National Polytechnique Félix Houphouët-Boigny
Ouagnina Hili: Institut National Polytechnique Félix Houphouët-Boigny
Edoh Katchekpele: Université de Kara

Indian Journal of Pure and Applied Mathematics, 2025, vol. 56, issue 1, 191-209

Abstract: Abstract We propose a new test to detect a change in the conditional mean of Nonparametric AutoRegressive processes. This test is based on the combining CUSUM and marked empirical processes. Our CUSUM test requires an estimator of the regression function to make it asymptotically distribution free under the no change null hypothesis. As a consequence, we obtain convergence of the developed test statistic. We show the asymptotic consistency of the test when the difference between the regression functions before and after the break is constant. We illustrate the applicability of our method by the means of Monte–Carlo simulation.

Keywords: Change point detection; Nonparametric estimation; Nonparametric test; $$\tau -$$ τ - mixing; 60G10; 60J65; 62E20; 62G05; 62G08; 62G10; 62G20 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13226-023-00471-6

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