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Characterization of a class of second-order density functions

Published online by Cambridge University Press:  14 July 2016

C. B. Mehr*
Affiliation:
Ohio University

Extract

Distributions of some random variables have been characterized by independence of certain functions of these random variables. For example, let X and Y be two independent and identically distributed random variables having the gamma distribution. Laha showed that U = X + Y and V = X | Y are also independent random variables. Lukacs showed that U and V are independently distributed if, and only if, X and Y have the gamma distribution. Ferguson characterized the exponential distribution in terms of the independence of X – Y and min (X, Y). The best-known of these characterizations is that first proved by Kac which states that if random variables X and Y are independent, then X + Y and X – Y are independent if, and only if, X and Y are jointly Gaussian with the same variance. In this paper, Kac's hypotheses have been somewhat modified. In so doing, we obtain a larger class of distributions which we shall call class λ1. A subclass λ0 of λ1 enjoys many nice properties of the Gaussian distribution, in particular, in non-linear filtering.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 

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References

[1] Laha, R. G. (1954) On a characterization of the gamma distribution. Ann. Math. Statist. 25, 784787.CrossRefGoogle Scholar
[2] Lukacs, E. (1955) A characterization of the gamma distribution. Ann. Math. Statist. 26, 319324.CrossRefGoogle Scholar
[3] Ferguson, T. S. (1964) A characterization of the exponential distribution. Ann. Math. Statist. 35, 11991207.CrossRefGoogle Scholar
[4] Kac, M. (1939) On a characterization of the normal distribution. Amer. J. Math. 6, 726728.CrossRefGoogle Scholar
[5] Vershik, A. M. (1964) Some characteristic properties of Gaussian stochastic processes. Theor. Probability Appl. (English translation) IX, 353356.CrossRefGoogle Scholar
[6] Rice, S. O. (1944) Mathematical analysis of random noise. Bell Syst. Tech. J. 23, 282332; 24, 48–156.CrossRefGoogle Scholar
[7] Mcfadden, J. A. (1966) A diagonal expansion in Gegenbauer polynomials for a class of second-order probability densities SIAM J. Appl. Math. 14, No. 6, 14331436.CrossRefGoogle Scholar
[8] Barrett, J. F. and Lampard, D. G. (1955) An expansion for some second-order distributions and its application to noise problems. IRE Trans. Information Theory IT-1, 1015.CrossRefGoogle Scholar
[9] Mehr, C. B. (1964) A class of wide-sense Markov processes. IBM Research Note NJ-66.Google Scholar
[10] Mehr, C. B. (1965) Optimum non-linear filters. IBM Research Report RJ-353.Google Scholar
[11] Nuttall, A. H. (1958) Theory and application of the separable class of random processes. Tech. Report 343, RL–E. M.I.T.Google Scholar