Publikation: Testing for Hermite rank in Gaussian subordination processes
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2016
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Journal of Computational and Graphical Statistics. 2016, 25(3), pp. 917-934. ISSN 1061-8600. eISSN 1537-2715. Available under: doi: 10.1080/10618600.2015.1056345
Zusammenfassung
Statistical inference for time series with long-range dependence is often based on the assumption of Gaussian subordination Xt = G(Zt). Although the Hermite rank m of G plays an essential role for statistical inference in these situations, the question of estimating m or of testing hypotheses about the Hermite rank has not been addressed in the literature. In this paper, a method is introduced for testing H0: m = 1 against H1: m > 1. This allows for deciding whether inference based on the usual assumption of m = 1 is appropriate. Simulations and data examples illustrate the method.
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510 Mathematik
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BERAN, Jan, Sven MÖHRLE, Sucharita GHOSH, 2016. Testing for Hermite rank in Gaussian subordination processes. In: Journal of Computational and Graphical Statistics. 2016, 25(3), pp. 917-934. ISSN 1061-8600. eISSN 1537-2715. Available under: doi: 10.1080/10618600.2015.1056345BibTex
@article{Beran2016-08-05Testi-33448,
year={2016},
doi={10.1080/10618600.2015.1056345},
title={Testing for Hermite rank in Gaussian subordination processes},
number={3},
volume={25},
issn={1061-8600},
journal={Journal of Computational and Graphical Statistics},
pages={917--934},
author={Beran, Jan and Möhrle, Sven and Ghosh, Sucharita}
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<dcterms:abstract xml:lang="eng">Statistical inference for time series with long-range dependence is often based on the assumption of Gaussian subordination X<sub>t</sub> = G(Z<sub>t</sub>). Although the Hermite rank m of G plays an essential role for statistical inference in these situations, the question of estimating m or of testing hypotheses about the Hermite rank has not been addressed in the literature. In this paper, a method is introduced for testing H<sub>0</sub>: m = 1 against H<sub>1</sub>: m > 1. This allows for deciding whether inference based on the usual assumption of m = 1 is appropriate. Simulations and data examples illustrate the method.</dcterms:abstract>
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