TY - GEN

T1 - Properties of the Conditional Mean Estimator in Poisson Noise

AU - Dytso, Alex

AU - Poor, H. Vincent

PY - 2019/8

Y1 - 2019/8

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85081095769&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85081095769&partnerID=8YFLogxK

U2 - 10.1109/ITW44776.2019.8989246

DO - 10.1109/ITW44776.2019.8989246

M3 - Conference contribution

T3 - 2019 IEEE Information Theory Workshop, ITW 2019

BT - 2019 IEEE Information Theory Workshop, ITW 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Information Theory Workshop, ITW 2019

Y2 - 25 August 2019 through 28 August 2019

ER -