Identifying the support of rectangular signals in Gaussian noise

Jiyao Kou

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 10, 3262-3289

Abstract: We consider the problem of identifying the support of the block signal in a sequence when both the length and the location of the block signal are unknown. The multivariate version of this problem is also considered, in which we try to identify the support of the rectangular signal in a hyper-rectangle. In this article, we greatly generalize the requirement in Jeng, Cai, and Li and in particular, we allow the length of the block signal to grow polynomially with the length of the sequence. A statistical boundary above which the identification is possible is presented and an asymptotically optimal and computationally efficient procedure is proposed under the assumption of Gaussian white noise. The problem of block signal identification is shown to have the same statistical difficulty as the corresponding problem of detection, in the sense that whenever we can detect the signal, we can identify the support of the signal. Some generalizations are also considered here: (1) the case of multiple block signals, (2) the robust identification problem where the noise distribution is unspecified, (3) the block signal identification problem under the exponential family setting.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1970771 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:52:y:2023:i:10:p:3262-3289

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2021.1970771

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().