On nonparametric ridge estimation for multivariate long-memory processes
Aufgrund von Vorbereitungen auf eine neue Version von KOPS, können am Montag, 6.2. und Dienstag, 7.2. keine Publikationen eingereicht werden. (Due to preparations for a new version of KOPS, no publications can be submitted on Monday, Feb. 6 and Tuesday, Feb. 7.)
| Type of Publication: | Journal article |
| Publication status: | Published |
| Author: | Beran, Jan; Telkmann, Klaus |
| Year of publication: | 2020 |
| Published in: | Lithuanian Mathematical Journal ; 60 (2020), 3. - pp. 291-314. - Springer. - ISSN 0363-1672. - eISSN 1573-8825 |
| DOI (citable link): | https://dx.doi.org/10.1007/s10986-020-09480-y |
| Summary: |
We consider nonparametric estimation of the ridge of a probability density function for multivariate linear processes with long-range dependence. We derive functional limit theorems for estimated eigenvectors and eigenvalues of the Hessian matrix. We use these results to obtain the weak convergence for the estimated ridge and asymptotic simultaneous confidence regions.
|
| Subject (DDC): | 510 Mathematics |
| Bibliography of Konstanz: | Yes |
| Refereed: | Unknown |
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BERAN, Jan, Klaus TELKMANN, 2020. On nonparametric ridge estimation for multivariate long-memory processes. In: Lithuanian Mathematical Journal. Springer. 60(3), pp. 291-314. ISSN 0363-1672. eISSN 1573-8825. Available under: doi: 10.1007/s10986-020-09480-y
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