Improving Inference for Spatial Signals by Contextual False Discovery Rates

Martin Gölz; Abdelhak M. Zoubir; Visa Koivunen

doi:10.1109/ICASSP43922.2022.9747596

Improving Inference for Spatial Signals by Contextual False Discovery Rates

Martin Gölz^*
, Abdelhak M. Zoubir
, Visa Koivunen

^*Corresponding author for this work

Visa Koivunen Group

Technische Universität Darmstadt

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

78 Downloads (Pure)

Abstract

A spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal's spatial behavior. The spatial domain is modeled as a fine discrete grid. The contextual local false discovery rate is computed for each grid point. A decision on the local state of the signal is made for each grid point, hence, many decisions are made simultaneously. A multiple hypothesis testing approach with false discovery rate control is used. The proposed procedure estimates the areas of interesting signal behavior with higher precision than existing methods. No tuning parameters have to be defined by the user.

Original language	English
Title of host publication	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Publisher	IEEE
Pages	5967-5971
Number of pages	5
ISBN (Electronic)	978-1-6654-0540-9
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9747596
Publication status	Published - 2022
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Acoustics, Speech, and Signal Processing - Singapore, Singapore Duration: 23 May 2022 → 27 May 2022

Publication series

Name	IEEE International Conference on Acoustics, Speech and Signal Processing
Volume	2022-May
ISSN (Print)	1520-6149

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP
Country/Territory	Singapore
City	Singapore
Period	23/05/2022 → 27/05/2022

Funding

The work of M. Gölz is supported by the German Research Foundation (DFG) under grant ZO 215/17-2. E-mail: [email protected].

Keywords

information fusion
local false discovery rate
multiple hypothesis testing
Sensor networks
spatial inference

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10.1109/ICASSP43922.2022.9747596

Gölz_Improving Inference for Spatial Signals by Contextual False Discovery RatesAccepted author manuscript, 534 KB

Cite this

Gölz, M., Zoubir, A. M., & Koivunen, V. (2022). Improving Inference for Spatial Signals by Contextual False Discovery Rates. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings (pp. 5967-5971). (IEEE International Conference on Acoustics, Speech and Signal Processing ; Vol. 2022-May). IEEE. https://doi.org/10.1109/ICASSP43922.2022.9747596

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title = "Improving Inference for Spatial Signals by Contextual False Discovery Rates",

abstract = "A spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal's spatial behavior. The spatial domain is modeled as a fine discrete grid. The contextual local false discovery rate is computed for each grid point. A decision on the local state of the signal is made for each grid point, hence, many decisions are made simultaneously. A multiple hypothesis testing approach with false discovery rate control is used. The proposed procedure estimates the areas of interesting signal behavior with higher precision than existing methods. No tuning parameters have to be defined by the user.",

keywords = "information fusion, local false discovery rate, multiple hypothesis testing, Sensor networks, spatial inference",

author = "Martin G{\"o}lz and Zoubir, \{Abdelhak M.\} and Visa Koivunen",

note = "Funding Information: The work of M. G{\"o}lz is supported by the German Research Foundation (DFG) under grant ZO 215/17-2. E-mail: [email protected]. Publisher Copyright: {\textcopyright} 2022 IEEE; IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ; Conference date: 23-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.1109/ICASSP43922.2022.9747596",

language = "English",

series = "IEEE International Conference on Acoustics, Speech and Signal Processing ",

publisher = "IEEE",

pages = "5967--5971",

booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",

address = "United States",

}

Gölz, M, Zoubir, AM & Koivunen, V 2022, Improving Inference for Spatial Signals by Contextual False Discovery Rates. in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE International Conference on Acoustics, Speech and Signal Processing , vol. 2022-May, IEEE, pp. 5967-5971, IEEE International Conference on Acoustics, Speech, and Signal Processing, Singapore, Singapore, 23/05/2022. https://doi.org/10.1109/ICASSP43922.2022.9747596

Improving Inference for Spatial Signals by Contextual False Discovery Rates. / Gölz, Martin; Zoubir, Abdelhak M.; Koivunen, Visa.
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE, 2022. p. 5967-5971 (IEEE International Conference on Acoustics, Speech and Signal Processing ; Vol. 2022-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Koivunen, Visa

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N2 - A spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal's spatial behavior. The spatial domain is modeled as a fine discrete grid. The contextual local false discovery rate is computed for each grid point. A decision on the local state of the signal is made for each grid point, hence, many decisions are made simultaneously. A multiple hypothesis testing approach with false discovery rate control is used. The proposed procedure estimates the areas of interesting signal behavior with higher precision than existing methods. No tuning parameters have to be defined by the user.

AB - A spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal's spatial behavior. The spatial domain is modeled as a fine discrete grid. The contextual local false discovery rate is computed for each grid point. A decision on the local state of the signal is made for each grid point, hence, many decisions are made simultaneously. A multiple hypothesis testing approach with false discovery rate control is used. The proposed procedure estimates the areas of interesting signal behavior with higher precision than existing methods. No tuning parameters have to be defined by the user.

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KW - multiple hypothesis testing

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ER -

Improving Inference for Spatial Signals by Contextual False Discovery Rates

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Keywords

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