Sensitivity of the Hermite rank

DOI10.1016/j.spa.2018.03.020zbMath1412.62114arXiv1710.01612OpenAlexW2795427234WikidataQ130033368 ScholiaQ130033368MaRDI QIDQ1730932

Shuyang Bai, Murad S. Taqqu

Publication date: 6 March 2019

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1710.01612

zbMATH Keywords

instabilitylimit theoremlong memorylong-range dependenceHermite rankpower rank

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05)

Related Items (4)

Rank-based change-point analysis for long-range dependent time series ⋮ Central limit theorems for nearly long range dependent subordinated linear processes ⋮ How the instability of ranks under long memory affects large-sample inference ⋮ Note on asymptotic behavior of spatial sign autocovariance matrices

Uses Software

• longmemo

Cites Work

• Multiple Wiener-Itô integrals. With applications to limit theorems
• How the instability of ranks under long memory affects large-sample inference
• Central limit theorems for non-linear functionals of Gaussian fields
• Bivariate symmetric statistics of long-range dependent observations
• Limit theorems for functionals of moving averages
• Asymptotic distributions of the sample mean, autocovariances, and autocorrelations of long-memory time series
• Stochastic Processes and Long Range Dependence
• Normal Approximations with Malliavin Calculus
• Long-Range Dependence and the Rank of Decompositions
• Towards a unified asymptotic theory for autoregression
• Convergence of integrated processes of arbitrary Hermite rank
• Non-central limit theorems for non-linear functional of Gaussian fields
• Long-Memory Processes
• Long-Range Dependence and Self-Similarity
• MULTIVARIATE LIMIT THEOREMS IN THE CONTEXT OF LONG‐RANGE DEPENDENCE
• A characterization of analytic functions of n real variables
• A Miniature Theory in Illustration of the Convolution Transform
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