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The signal-noise problem — a solution for the case that signal and noise are Gaussian and independent

Published online by Cambridge University Press:  14 July 2016

Michael F. Driscoll*
Affiliation:
Arizona State University

Abstract

A solution is obtained for the signal-noise problem X = M + Z in which M and Z are independent Gaussian processes. Conditions on the processes are given which insure that the best estimate under generalized square — error loss is the conditional mean of M given X = x. A sequence of approximators of the best estimate is also given.

Type
Short Communications
Copyright
Copyright © Applied Probability Trust 1975 

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References

[1] Aronszajn, N. (1950) Theory of reproducing kernels. Trans. Amer. Math. Soc. 68, 337404.Google Scholar
[2] Doob, J. L. (1953) Stochastic Processes. Wiley, New York.Google Scholar
[3] Driscoll, M. F. (1971) Estimation of the mean value function of a Gaussian process . Ph.D. dissertation, University of Arizona.Google Scholar
[4] Driscoll, M. F. (1973) The reproducing kernel Hilbert space structure of the sample paths of a Gaussian process. Z. Wahrscheinlichkeitsth. 26, 309316.Google Scholar
[5] Loeve, M. (1963) Probability Theory (3rd. ed.). Van Nostrand, Priceton, N. J. Google Scholar
[6] Parzen, E. (1959) Statistical Inference on Time Series by Hilbert Space Methods, I. Dept. of Statist., Stanford Univ., Technical Report No. 23, January 2, 1959.Google Scholar
[7] Parzen, E. (1963) Probability density functionals and reproducing kernel Hilbert spaces. Time Series Analysis. Rosenblatt, M., Ed. Wiley, New York. 155169.Google Scholar