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- W4316830038 abstract "We investigate the validity of two resampling techniques when carrying out inference on the underlying unknown copula using a recently proposed class of smooth, possibly data-adaptive nonparametric estimators that contains empirical Bernstein copulas (and thus the empirical beta copula). Following cite{KirSegTsu21}, the first resampling technique is based on drawing samples from the smooth estimator and can only can be used in the case of independent observations. The second technique is a smooth extension of the so-called sequential dependent multiplier bootstrap and can thus be used in a time series setting and, possibly, for change-point analysis. The two studied resampling schemes are applied to confidence interval construction and the offline detection of changes in the cross-sectional dependence of multivariate time series, respectively. Monte Carlo experiments confirm the possible advantages of such smooth inference procedures over their non-smooth counterparts. A by-product of this work is the study of the weak consistency and finite-sample performance of two classes of smooth estimators of the first-order partial derivatives of a copula which can have applications in mean and quantile regression." @default.
- W4316830038 created "2023-01-17" @default.
- W4316830038 creator A5071107317 @default.
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- W4316830038 date "2023-01-13" @default.
- W4316830038 modified "2023-10-01" @default.
- W4316830038 title "Resampling techniques for a class of smooth, possibly data-adaptive empirical copulas" @default.
- W4316830038 doi "https://doi.org/10.48550/arxiv.2301.05495" @default.
- W4316830038 hasPublicationYear "2023" @default.
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