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• W3033964493 abstract "A weakly dependent time series regression model with multivariate covariates and univariate observations is considered, for which we develop a procedure to detect whether the nonparametric conditional mean function is stable in time against change point alternatives. Our proposal is based on a modified CUSUM type test procedure, which uses a sequential marked empirical process of residuals. We show weak convergence of the considered process to a centered Gaussian process under the null hypothesis of no change in the mean function and a stationarity assumption. This requires some sophisticated arguments for sequential empirical processes of weakly dependent variables. As a consequence we obtain convergence of Kolmogorov-Smirnov and Cramér-von Mises type test statistics. The proposed procedure acquires a very simple limiting distribution and nice consistency properties, features from which related tests are lacking. We moreover suggest a bootstrap version of the procedure and discuss its applicability in the case of unstable variances." @default.
• W3033964493 created "2020-06-12" @default.
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• W3033964493 date "2020-01-01" @default.
• W3033964493 modified "2023-09-25" @default.
• W3033964493 title "Consistent nonparametric change point detection combining CUSUM and marked empirical processes" @default.
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• W3033964493 doi "https://doi.org/10.1214/20-ejs1715" @default.
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