Consistent Estimation of Conditional Cumulants in the Empirical Bayes Framework (Extended Abstract)

Tang Liu; Alex Dytso; H. Vincent Poor; Shlomo Shamai

doi:10.1109/IEEECONF56349.2022.10052066

Consistent Estimation of Conditional Cumulants in the Empirical Bayes Framework (Extended Abstract)

Tang Liu, Alex Dytso, H. Vincent Poor, Shlomo Shamai

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Consider a noisy observation Y=X+N where X is a random variable, and N is a Gaussian random variable with zero mean, variance s2, independent from X. The object of this work is to construct a consistent estimator for the conditional cumulants of the random variable X given the observation Y=y, in the empirical Bayes framework. Cu-mulants are important statistical quantities that provide useful alternatives to moments and have a variety of applications [1]-[4].

Original language	English
Title of host publication	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Editors	Michael B. Matthews
Pages	1036-1037
Number of pages	2
ISBN (Electronic)	9781665459068
DOIs	https://doi.org/10.1109/IEEECONF56349.2022.10052066
State	Published - 2022
Event	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States Duration: 31 Oct 2022 → 2 Nov 2022

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2022-October

Conference

Conference	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/Territory	United States
City	Virtual, Online
Period	31/10/22 → 2/11/22

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF56349.2022.10052066

Cite this

Liu, T., Dytso, A., Poor, H. V., & Shamai, S. (2022). Consistent Estimation of Conditional Cumulants in the Empirical Bayes Framework (Extended Abstract). In M. B. Matthews (Ed.), 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 (pp. 1036-1037). (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2022-October). https://doi.org/10.1109/IEEECONF56349.2022.10052066

@inproceedings{240b4b24ad4f48e08ee943ff698b25ad,

title = "Consistent Estimation of Conditional Cumulants in the Empirical Bayes Framework (Extended Abstract)",

abstract = "Consider a noisy observation Y=X+N where X is a random variable, and N is a Gaussian random variable with zero mean, variance s2, independent from X. The object of this work is to construct a consistent estimator for the conditional cumulants of the random variable X given the observation Y=y, in the empirical Bayes framework. Cu-mulants are important statistical quantities that provide useful alternatives to moments and have a variety of applications [1]-[4].",

author = "Tang Liu and Alex Dytso and Poor, {H. Vincent} and Shlomo Shamai",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 ; Conference date: 31-10-2022 Through 02-11-2022",

year = "2022",

doi = "10.1109/IEEECONF56349.2022.10052066",

language = "الإنجليزيّة",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

pages = "1036--1037",

editor = "Matthews, {Michael B.}",

booktitle = "56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022",

}

Liu, T, Dytso, A, Poor, HV & Shamai, S 2022, Consistent Estimation of Conditional Cumulants in the Empirical Bayes Framework (Extended Abstract). in MB Matthews (ed.), 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022. Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2022-October, pp. 1036-1037, 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022, Virtual, Online, United States, 31/10/22. https://doi.org/10.1109/IEEECONF56349.2022.10052066

Consistent Estimation of Conditional Cumulants in the Empirical Bayes Framework (Extended Abstract). / Liu, Tang; Dytso, Alex; Poor, H. Vincent et al.
56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022. ed. / Michael B. Matthews. 2022. p. 1036-1037 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Poor, H. Vincent

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N2 - Consider a noisy observation Y=X+N where X is a random variable, and N is a Gaussian random variable with zero mean, variance s2, independent from X. The object of this work is to construct a consistent estimator for the conditional cumulants of the random variable X given the observation Y=y, in the empirical Bayes framework. Cu-mulants are important statistical quantities that provide useful alternatives to moments and have a variety of applications [1]-[4].

AB - Consider a noisy observation Y=X+N where X is a random variable, and N is a Gaussian random variable with zero mean, variance s2, independent from X. The object of this work is to construct a consistent estimator for the conditional cumulants of the random variable X given the observation Y=y, in the empirical Bayes framework. Cu-mulants are important statistical quantities that provide useful alternatives to moments and have a variety of applications [1]-[4].

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M3 - منشور من مؤتمر

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1036

EP - 1037

BT - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022

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T2 - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022

Y2 - 31 October 2022 through 2 November 2022

ER -

Consistent Estimation of Conditional Cumulants in the Empirical Bayes Framework (Extended Abstract)

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