Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288278 
Year of Publication: 
2020
Citation: 
[Journal:] AStA Advances in Statistical Analysis [ISSN:] 1863-818X [Volume:] 104 [Issue:] 3 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 417-457
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
In many situations, it is crucial to estimate the variance properly. Ordinary variance estimators perform poorly in the presence of shifts in the mean. We investigate an approach based on non-overlapping blocks, which yields good results in change-point scenarios. We show the strong consistency and the asymptotic normality of such blocks-estimators of the variance under independence. Weak consistency is shown for short-range dependent strictly stationary data. We provide recommendations on the appropriate choice of the block size and compare this blocks-approach with difference-based estimators. If level shifts occur frequently and are rather large, the best results can be obtained by adaptive trimming of the blocks.
Subjects: 
Blockwise estimation
Change-point
Trimmed mean
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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