scholarly journals On revelation transforms that characterize probability distributions

1993 ◽  
Vol 6 (4) ◽  
pp. 345-357 ◽  
Author(s):  
S. Chukova ◽  
B. Dimitrov ◽  
J.-P. Dion
Keyword(s):  
Exponential Distribution ◽  
Probability Distributions ◽  
Random Variables ◽  
Memory Property ◽  
Lack Of Memory Property

A characterization of exponential, geometric and of distributions with almost-lack-of-memory property, based on the “revelation transform of probability distributions” and “relevation of random variables” is discussed. Known characterizations of the exponential distribution on the base of relevation transforms given by Grosswald et al. [4], and Lau and Rao [7] are obtained under weakened conditions and the proofs are simplified. A characterization the class of almost-lack-of-memory distributions through the relevation is specified.

An unreliable server characterization of the exponential distribution

1994 ◽  
Vol 31 (1) ◽  
pp. 274-279 ◽  
Author(s):  
Janos Galambos ◽  
Charles Hagwood
Keyword(s):  
Distribution Function ◽  
Exponential Distribution ◽  
Service Time ◽  
Unreliable Server ◽  
Memory Property ◽  
Lack Of Memory Property

Consider a workstation with one server, performing jobs with a service time, Y, having distribution function, G(t). Assume that the station is unreliable, in that it occasionally breaks down. The station is instantaneously repaired, and the server restarts the uncompleted job from the beginning. Let T denote the time it takes to complete each job. If G(t) is exponential with parameter A, then because of the lack-of-memory property of the exponential, P (T > t) = Ḡ(t) =exp(−γt), irrespective of when and how the failures occur. This property also characterizes the exponential distribution.

An unreliable server characterization of the exponential distribution

1994 ◽  
Vol 31 (01) ◽  
pp. 274-279 ◽  
Author(s):  
Janos Galambos ◽  
Charles Hagwood
Keyword(s):  
Distribution Function ◽  
Exponential Distribution ◽  
Service Time ◽  
Unreliable Server ◽  
Memory Property ◽  
Lack Of Memory Property

Consider a workstation with one server, performing jobs with a service time, Y, having distribution function, G(t). Assume that the station is unreliable, in that it occasionally breaks down. The station is instantaneously repaired, and the server restarts the uncompleted job from the beginning. Let T denote the time it takes to complete each job. If G(t) is exponential with parameter A, then because of the lack-of-memory property of the exponential, P (T > t) = Ḡ(t) =exp(−γt), irrespective of when and how the failures occur. This property also characterizes the exponential distribution.

On distributions having the almost-lack-of-memory property

1992 ◽  
Vol 29 (3) ◽  
pp. 691-698 ◽  
Author(s):  
S. Chukova ◽  
B. Dimitrov
Keyword(s):  
Random Variables ◽  
Unreliable Server ◽  
Memory Property ◽  
The Given ◽  
Lack Of Memory Property

It is shown that random variables X exist, not exponentially or geometrically distributed, such thatP{X – b ≧ x | X ≧ b} = P{X ≧ x}for all x > 0 and infinitely many different values of b. A class of distributions having the given property is exhibited. We call them ALM distributions, since they almost have the lack-of-memory property. For a given subclass of these distributions some phenomena relating to service by an unreliable server are discussed.

A characterization of the negative exponential distribution with application to reliability theory

1981 ◽  
Vol 18 (3) ◽  
pp. 652-659 ◽  
Author(s):  
M. J. Phillips
Keyword(s):  
Exponential Distribution ◽  
Random Variables ◽  
Reliability Theory ◽  
The Other ◽  
Independent Random Variables ◽  
System Availability ◽  
Failure Times ◽  
Negative Exponential Distribution ◽  
Negative Exponential

The negative exponential distribution is characterized in terms of two independent random variables. Only one of the random variables has a negative exponential distribution whilst the other can belong to a wide class of distributions. This result is then applied to two models for the reliability of a system of two modules subject to revealed and unrevealed faults to show when the models are equivalent. It is also shown, under certain conditions, that the system availability is only independent of the distribution of revealed failure times in one module when unrevealed failure times in the other module have a negative exponential distribution.

Some new approaches to probability distributions

1980 ◽  
Vol 12 (04) ◽  
pp. 903-921 ◽  
Author(s):  
S. Kotz ◽  
D. N. Shanbhag
Keyword(s):  
Probability Distributions ◽  
Random Variables ◽  
Representation Theorems ◽  
Practical Applications ◽  
New Approaches ◽  
Characterization Of Distributions ◽  
Stability Theorems ◽  
Conditional Expectations

We develop some approaches to the characterization of distributions of real-valued random variables, useful in practical applications, in terms of conditional expectations and hazard measures. We prove several representation theorems generalizing earlier results, and establish stability theorems for two general characteristics introduced in this paper.

A characterization of the negative exponential distribution with application to reliability theory

1981 ◽  
Vol 18 (03) ◽  
pp. 652-659 ◽  
Author(s):  
M. J. Phillips
Keyword(s):  
Exponential Distribution ◽  
Random Variables ◽  
Reliability Theory ◽  
The Other ◽  
Independent Random Variables ◽  
System Availability ◽  
Failure Times ◽  
Negative Exponential Distribution ◽  
Negative Exponential

The negative exponential distribution is characterized in terms of two independent random variables. Only one of the random variables has a negative exponential distribution whilst the other can belong to a wide class of distributions. This result is then applied to two models for the reliability of a system of two modules subject to revealed and unrevealed faults to show when the models are equivalent. It is also shown, under certain conditions, that the system availability is only independent of the distribution of revealed failure times in one module when unrevealed failure times in the other module have a negative exponential distribution.

Characterizations of the Exponential Distribution via the Blocking Time in a Queueing System with an Unreliable Server

1998 ◽  
Vol 35 (1) ◽  
pp. 236-239 ◽  
Author(s):  
Jian-Lun Xu
Keyword(s):  
Exponential Distribution ◽  
Service Time ◽  
Coefficient Of Variation ◽  
Queueing System ◽  
Random Variables ◽  
Unreliable Server ◽  
Failure Times ◽  
Server Failure

The characterization of the exponential distribution via the coefficient of the variation of the blocking time in a queueing system with an unreliable server, as given by Lin (1993), is improved by substantially weakening the conditions. Based on the coefficient of variation of certain random variables, including the blocking time, the normal service time and the minimum of the normal service and the server failure times, two new characterizations of the exponential distribution are obtained.

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.

A characterization of the geometric distribution

1974 ◽  
Vol 11 (03) ◽  
pp. 609-611
Author(s):  
A. C. Dallas
Keyword(s):  
Geometric Distribution ◽  
Conditional Variance ◽  
Memory Property ◽  
Lack Of Memory Property

The geometric distribution is characterized. The lack of memory property is replaced by the constancy of the conditional variance. Then the characterization is obtained.

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