Characterization of the Gaussian distribution on groups by the independence of linear statistics

1990 ◽  
Vol 31 (2) ◽  
pp. 336-345 ◽  
Author(s):  
G. M. Fel'dman
Keyword(s):  
Gaussian Distribution ◽  
Linear Statistics

Characterization of a class of second-order density functions

1967 ◽  
Vol 4 (1) ◽  
pp. 123-129 ◽  
Author(s):  
C. B. Mehr
Keyword(s):  
Exponential Distribution ◽  
Gamma Distribution ◽  
Gaussian Distribution ◽  
Random Variables ◽  
Second Order ◽  
Independent Random Variables ◽  
Density Functions ◽  
Linear Filtering ◽  
Non Linear

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.

scholarly journals Subdifferential characterization of probability functions under Gaussian distribution

2018 ◽  
Vol 174 (1-2) ◽  
pp. 167-194 ◽  
Author(s):  
Abderrahim Hantoute ◽  
René Henrion ◽  
Pedro Pérez-Aros
Keyword(s):  
Gaussian Distribution ◽  
Probability Functions
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