THE EFFECT OF THE EXPONENTIAL DISTRIBUTION FUNCTION ON THE ELECTROPHORETIC CONTRIBUTION TO THE CONDUCTANCE OF 1-1 ELECTROLYTES1

1962 ◽  
Vol 66 (3) ◽  
pp. 477-482 ◽  
Author(s):  
David J. Karl ◽  
James L. Dye
Keyword(s):  
Distribution Function ◽  
Exponential Distribution

The Approximation of Sum of Independent Distributions by the Erlang Distribution Function

2002 ◽  
Vol 7 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Antanas Karoblis
Keyword(s):  
Distribution Function ◽  
Exponential Distribution ◽  
Queueing Theory ◽  
Erlang Distribution ◽  
Estimation Of Error

The exponential distribution and the Erlang distribution function are been used in numerous areas of mathematics, and specifically in the queueing theory. Such and similar applications emphasize the importance of estimation of error of approximation by the Erlang distribution function. The article gives an analysis and technique of error’s estimation of an accuracy of such approximation, especially in some specific cases.

STOCHASTIC TRANSMISSION DYNAMICS OF COVID-19 WITHIN A DENSITY DEPENDENT POPULATION

10.33003/659 ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 567-573
Author(s):  
Lubem M. Kwaghkor ◽  
Stephen E. Onah ◽  
Ibrahim G. Basi ◽  
Theophilus Danjuma
Keyword(s):  
Distribution Function ◽  
Exponential Distribution ◽  
Severe Disease ◽  
The Elderly ◽  
Control Measures ◽  
Special Treatment ◽  
Susceptible Population ◽  
Density Dependent ◽  
Infected People ◽  
Population Sizes

Coronavirus diseases (COVID-19) is a respiratory disease. Most infected people are known to develop mild to moderate symptoms and recover without requiring special treatment except for those who have underlying medical conditions and the elderly have a higher risk of developing severe disease. This research is aimed at studying the probable spread of the COVID-19 virus within a completely susceptible density-dependent population using a modified exponential distribution function. The modified exponential distribution function was extended to include the Basic Reproduction Number which was computed using the Nigerian COVID-19 index cases from 27th February to 18th April, 2020 to be. Various interesting results were obtained for the including the time period for the spread for different population sizes. The duration of the spread of the virus is from 4 to 7 hours with an average of 5.5 hours. This indicates that, for one infectious person with  to enter a completely susceptible population of size , the virus can spread through the entire population in about  hours if no control measures are in place.

Characterizations of the exponential distribution by order statistics

1974 ◽  
Vol 11 (03) ◽  
pp. 605-608 ◽  
Author(s):  
J. S. Huang
Keyword(s):  
Distribution Function ◽  
Order Statistics ◽  
Exponential Distribution ◽  
Special Cases ◽  
Characterization Theorems

Let X 1,n ≦ … ≦ Xn, n be the order statistics of a sample of size n from a distribution function F. Desu (1971) showed that if for all n ≧ 2, nX 1,n is identically distributed as X 1, 1, then F is the exponential distribution (or else F degenerates). The purpose of this note is to point out that special cases of known characterization theorems already constitute an improvement over this result. We show that the characterization is preserved if “identically distributed” is weakened to “having identical (finite) expectation”, and “for all n ≧ 2” is weakened to “for a sequence of n's with divergent sum of reciprocals”.

scholarly journals The Characteristic Function Property of Mixture Negative Binomial-Exponential Distribution

2018 ◽  
Vol 3 (4) ◽  
pp. 178
Author(s):  
Dodi Devianto ◽  
Sarah Sarah ◽  
Siska Dwi Kumala ◽  
Maiyastri Maiyastri
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Characteristic Function ◽  
Exponential Distribution ◽  
Negative Binomial Distribution ◽  
Probability Distribution Function ◽  
Negative Binomial ◽  
Stieltjes Transform ◽  
Function Property ◽  
New Distribution

This paper introduces a new distribution by mixing the negative binomial distribution and exponential distribution namely negative binomial-exponential (NB-E) distribution. In is given the probability distribution function of NB-E distribution and its characteristic function by using Fourier-Stieltjes transform. In addition we present the some properties of characteristic function from NB-E distribution.

Random arcs on the circle

1978 ◽  
Vol 15 (04) ◽  
pp. 774-789 ◽  
Author(s):  
Andrew F. Siegel
Keyword(s):  
Integral Equation ◽  
Distribution Function ◽  
Exponential Distribution ◽  
Asymptotic Distribution ◽  
Cumulative Distribution Function ◽  
Cumulative Distribution

Place n arcs of equal lengths randomly on the circumference of a circle, and let C denote the proportion covered. The moments of C (moments of coverage) are found by solving a recursive integral equation, and a formula is derived for the cumulative distribution function. The asymptotic distribution of C for large n is explored, and is shown to be related to the exponential distribution.

scholarly journals The uncertainty measure for q-exponential distribution function

2013 ◽  
Vol 58 (13) ◽  
pp. 1524-1528 ◽  
Author(s):  
CongJie Ou ◽  
Aziz El Kaabouchi ◽  
QiuPing Alexandre Wang ◽  
JinCan Chen
Keyword(s):  
Distribution Function ◽  
Exponential Distribution ◽  
Uncertainty Measure

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.

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