scholarly journals On Cumulative Entropies in Terms of Moments of Order Statistics

2021 ◽  
Author(s):  
Narayanaswamy Balakrishnan ◽  
Francesco Buono ◽  
Maria Longobardi
Keyword(s):  
Order Statistics ◽  
Random Variable ◽  
Upper Bounds ◽  
Lower And Upper Bounds ◽  
Continuous Random Variable ◽  
Absolutely Continuous ◽  
Moments Of Order Statistics

AbstractIn this paper, relations between some kinds of cumulative entropies and moments of order statistics are established. By using some characterizations and the symmetry of a non-negative and absolutely continuous random variable X, lower and upper bounds for entropies are obtained and illustrative examples are given. By the relations with the moments of order statistics, a method is shown to compute an estimate of cumulative entropies and an application to testing whether data are exponentially distributed is outlined.

A characterization of the exponential distribution

1972 ◽  
Vol 9 (02) ◽  
pp. 457-461 ◽  
Author(s):  
M. Ahsanullah ◽  
M. Rahman
Keyword(s):  
Probability Distribution ◽  
Order Statistics ◽  
Lebesgue Measure ◽  
Random Variable ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
Positive Random Variable ◽  
Absolutely Continuous ◽  
Necessary And Sufficient

A necessary and sufficient condition based on order statistics that a positive random variable having an absolutely continuous probability distribution (with respect to Lebesgue measure) will be exponential is given.

scholarly journals Bounds for the variance of the busy period of the M/G/∞ queue

1984 ◽  
Vol 16 (4) ◽  
pp. 929-932 ◽  
Author(s):  
M. F. Ramalhoto
Keyword(s):  
Distribution Function ◽  
Lower Bounds ◽  
Service Time ◽  
Time Distribution ◽  
Busy Period ◽  
Random Variable ◽  
Upper Bounds ◽  
Upper And Lower Bounds ◽  
Service Time Distribution ◽  
Lower And Upper Bounds

Some bounds for the variance of the busy period of an M/G/∞ queue are calculated as functions of parameters of the service-time distribution function. For any type of service-time distribution function, upper and lower bounds are evaluated in terms of the intensity of traffic and the coefficient of variation of the service time. Other lower and upper bounds are derived when the service time is a NBUE, DFR or IMRL random variable. The variance of the busy period is also related to the variance of the number of busy periods that are initiated in (0, t] by renewal arguments.

Permutation probabilities for gamma random variables

1983 ◽  
Vol 20 (4) ◽  
pp. 822-834 ◽  
Author(s):  
Robert J. Henery
Keyword(s):  
Order Statistics ◽  
Negative Binomial ◽  
Random Variables ◽  
Upper Bounds ◽  
Alternative Formulation ◽  
Confidence Limits ◽  
Lower And Upper Bounds ◽  
Sample Variances

The order statistics of a set of independent gamma variables, in general not identically distributed, may serve as a basis for ordering players in a hypothetical game. An alternative formulation in terms of negative binomial variables leads to an expression for the probability that the random gammas are in a given order. This expression may contain rather many terms and some approximations are discussed — firstly as the gamma parameters αi tend to equality with all ni the same, and secondly when the probability of an inversion is small. In another interpretation the probabilities discussed arise in the statement of confidence limits for the ratios of population variances, and here the inversion probability is small enough usually that lower and upper bounds may be given for the probability that the sample variances occur in their expected order. These bounds are calculated from the probability that two variables are in expected order, and for gamma variables this probability is obtained from the F-distribution.

scholarly journals Bounds for the variance of the busy period of the M/G/∞ queue

1984 ◽  
Vol 16 (04) ◽  
pp. 929-932
Author(s):  
M. F. Ramalhoto
Keyword(s):  
Distribution Function ◽  
Lower Bounds ◽  
Service Time ◽  
Time Distribution ◽  
Busy Period ◽  
Random Variable ◽  
Upper Bounds ◽  
Upper And Lower Bounds ◽  
Service Time Distribution ◽  
Lower And Upper Bounds

Some bounds for the variance of the busy period of an M/G/∞ queue are calculated as functions of parameters of the service-time distribution function. For any type of service-time distribution function, upper and lower bounds are evaluated in terms of the intensity of traffic and the coefficient of variation of the service time. Other lower and upper bounds are derived when the service time is a NBUE, DFR or IMRL random variable. The variance of the busy period is also related to the variance of the number of busy periods that are initiated in (0, t] by renewal arguments.

Preservation of Some Ageing Properties by Order Statistics

1993 ◽  
Vol 7 (3) ◽  
pp. 437-440 ◽  
Author(s):  
Neeraj Misra ◽  
M. Manoharan ◽  
Harshinder Singh
Keyword(s):  
Order Statistics ◽  
Density Function ◽  
Failure Rate ◽  
Order Statistic ◽  
Random Variable ◽  
Continuous Random Variable ◽  
Life Distribution ◽  
Increasing Failure Rate ◽  
Decreasing Failure Rate

Let X be a continuous random variable denoting the lifetime of a unit. Let Xk:n denote the kth order statistic based on n independent random observations on X. It has been shown that if Xk:n has decreasing failure rate (DFR) for some k, 1 ≤ k ≤ n, then X is DFR. For n ≥ 2, if Xk:n has increasing failure rate (IFR), then Xk:n–1 is also IFR, and if Xk:n is DFR, then Xk:n+1 is also DFR. The log concavity of the density, function is shown to be preserved by the kth order statistic. It has been established that if the density function of Xk:n is log convex then the density function of Xk:n+1 is also log convex. Because a k-out-of-n system of i.i.d. components each having a life distribution that of X has lifetime Xn-k+1:n the results have applications in the study of such systems.

Permutation probabilities for gamma random variables

1983 ◽  
Vol 20 (04) ◽  
pp. 822-834 ◽  
Author(s):  
Robert J. Henery
Keyword(s):  
Order Statistics ◽  
Negative Binomial ◽  
Random Variables ◽  
Upper Bounds ◽  
Alternative Formulation ◽  
Confidence Limits ◽  
Lower And Upper Bounds ◽  
Sample Variances

The order statistics of a set of independent gamma variables, in general not identically distributed, may serve as a basis for ordering players in a hypothetical game. An alternative formulation in terms of negative binomial variables leads to an expression for the probability that the random gammas are in a given order. This expression may contain rather many terms and some approximations are discussed — firstly as the gamma parameters αi tend to equality with all ni the same, and secondly when the probability of an inversion is small. In another interpretation the probabilities discussed arise in the statement of confidence limits for the ratios of population variances, and here the inversion probability is small enough usually that lower and upper bounds may be given for the probability that the sample variances occur in their expected order. These bounds are calculated from the probability that two variables are in expected order, and for gamma variables this probability is obtained from the F-distribution.

A characterization of the exponential distribution

1972 ◽  
Vol 9 (2) ◽  
pp. 457-461 ◽  
Author(s):  
M. Ahsanullah ◽  
M. Rahman
Keyword(s):  
Probability Distribution ◽  
Order Statistics ◽  
Lebesgue Measure ◽  
Random Variable ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
Positive Random Variable ◽  
Absolutely Continuous ◽  
Necessary And Sufficient

A necessary and sufficient condition based on order statistics that a positive random variable having an absolutely continuous probability distribution (with respect to Lebesgue measure) will be exponential is given.

On the Three-Moment Approximation of a General Distribution by a Coxian Distribution

1988 ◽  
Vol 2 (2) ◽  
pp. 257-261 ◽  
Author(s):  
M. C. Van Der Heijden
Keyword(s):  
Coefficient Of Variation ◽  
Random Variable ◽  
Upper Bounds ◽  
General Distribution ◽  
Positive Random Variable ◽  
Lower And Upper Bounds ◽  
The Third ◽  
Coxian Distribution ◽  
Moment Approximation ◽  
Third Moment

The Coxian-2 distribution is a very useful distribution for queuing and reliability analysis. It is important to know when a general probability distribution can be approximated by a Coxian-2 distribution by fitting the first three moments. For a positive random variable with a squared coefficient of variation larger than 1, a lower bound on its third moment is known for which a three-moment fit exists. To complete the figure, in this note lower and upper bounds on the third moment are derived when the squared coefficient of variation is between 0.5 and 1. Also, we characterize the C2-distributions that correspond to these bounds.

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