Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Axt, Ieva | - |
dc.contributor.author | Fried, Roland | - |
dc.date.accessioned | 2021-03-30T07:14:07Z | - |
dc.date.available | 2021-03-30T07:14:07Z | - |
dc.date.issued | 2020-04-01 | - |
dc.identifier.uri | http://hdl.handle.net/2003/40121 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-21998 | - |
dc.description.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. | en |
dc.language.iso | en | de |
dc.relation.ispartofseries | AStA Adv Stat Anal;104 | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Blockwise estimation | en |
dc.subject | Change-point | en |
dc.subject | Trimmed mean | en |
dc.subject.ddc | 310 | - |
dc.title | On variance estimation under shifts in the mean | en |
dc.type | Text | de |
dc.type.publicationtype | article | de |
dcterms.accessRights | open access | - |
eldorado.secondarypublication | true | de |
eldorado.secondarypublication.primaryidentifier | https://doi.org/10.1007/s10182-020-00366-5 | de |
eldorado.secondarypublication.primarycitation | Axt, I., Fried, R. On variance estimation under shifts in the mean. AStA Adv Stat Anal 104, 417–457 (2020). | de |
Appears in Collections: | Lehrstuhl Mathematische Statistik und naturwissenschaftliche Anwendungen |
Files in This Item:
File | Description | Size | Format | |
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Axt-Fried2020_Article_OnVarianceEstimationUnderShift.pdf | 4.58 MB | Adobe PDF | View/Open |
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