Properties of the Conditional Mean Estimator in Poisson Noise

Alex Dytso, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper considers estimation of a random variable in Poisson noise. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the scaling coefficient, the dark current parameter, the distribution of the input random variable and channel realizations.With respect to the scaling coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the derivative of the conditional mean estimator with respect to the dark current parameter is proportional to the conditional variance. Moreover, a version of score function is proposed and a Tweedy-like formula for the conditional expectation is recovered.With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in the mean squared error, then the input distribution is approximately gamma in the Lévy metric.

Original languageEnglish (US)
Title of host publication2019 IEEE Information Theory Workshop, ITW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538669006
DOIs
StatePublished - Aug 2019
Event2019 IEEE Information Theory Workshop, ITW 2019 - Visby, Sweden
Duration: Aug 25 2019Aug 28 2019

Publication series

Name2019 IEEE Information Theory Workshop, ITW 2019

Conference

Conference2019 IEEE Information Theory Workshop, ITW 2019
CountrySweden
CityVisby
Period8/25/198/28/19

All Science Journal Classification (ASJC) codes

  • Software
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Information Systems

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