Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Idris A. Eckley"'
Autor:
Giuseppe Dilillo, Kes Ward, Idris A. Eckley, Paul Fearnhead, Riccardo Crupi, Yuri Evangelista, Andrea Vacchi, Fabrizio Fiore
Publikováno v:
The Astrophysical Journal, Vol 962, Iss 2, p 137 (2024)
We describe how a novel online change-point detection algorithm, called Poisson-FOCuS, can be used to optimally detect gamma-ray bursts within the computational constraints imposed by miniaturized satellites such as the upcoming HERMES-Pathfinder con
Externí odkaz:
https://doaj.org/article/e8116116bb3e4169bdb04c661e91bcd6
Publikováno v:
Journal of Statistical Software, Vol 90, Iss 1, Pp 1-19 (2019)
This paper describes the R package mvLSW. The package contains a suite of tools for the analysis of multivariate locally stationary wavelet (LSW) time series. Key elements include: (i) the simulation of multivariate LSW time series for a given multiv
Externí odkaz:
https://doaj.org/article/6f65c34dac1f487c83d9d212a1508cbb
Autor:
Rebecca Killick, Idris A. Eckley
Publikováno v:
Journal of Statistical Software, Vol 58, Iss 1, Pp 1-19 (2014)
One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been developed to provide users with a choice of multiple changepoint search methods to us
Externí odkaz:
https://doaj.org/article/8f02edc945464986b35a2c39e2f34139
Autor:
Idris A. Eckley, Guy P. Nason
Publikováno v:
Journal of Statistical Software, Vol 43, Iss 03 (2011)
Locally stationary process representations have recently been proposed and applied to both time series and image analysis applications. This article describes an implementation of the locally stationary two-dimensional wavelet process approach in R.
Externí odkaz:
https://doaj.org/article/76544646472547ab8564e604781d4f8c
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal. 15:494-508
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal.
Publikováno v:
Lancaster University-Pure
Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a movi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f709b0613d1607de4910574f635e8c16
https://eprints.lancs.ac.uk/id/eprint/190722/
https://eprints.lancs.ac.uk/id/eprint/190722/
Different environmental variables are often monitored using different sampling rates; examples include half-hourly weather station measurements, daily (Formula presented.) data, and six-day satellite data. Further when researchers want to combine the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b71662bef71f65f2bb2cc5844145466
https://doi.org/10.1002/env.2762
https://doi.org/10.1002/env.2762
Publikováno v:
Lancaster University-Pure
In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams. We consider the problem of detecting collective anomalies, corresponding to intervals where one or more of the data streams behave
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01834ea7048476655e45ab18a29a330f
https://doi.org/10.1080/10618600.2021.1987257
https://doi.org/10.1080/10618600.2021.1987257
Publikováno v:
Statistics and Computing. 32
The ability to quickly and accurately detect anomalous structure within data sequences is an inference challenge of growing importance. This work extends recently proposed post-hoc (offline) anomaly detection methodology to the sequential setting. Th