Bayesian prediction of order statistics based on k-record values from exponential distribution

Statistics ◽  
2010 ◽  
Vol 45 (4) ◽  
pp. 375-387 ◽  
Author(s):  
Jafar Ahmadi ◽  
S. M.T.K. MirMostafaee ◽  
N. Balakrishnan
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Record Values ◽  
Bayesian Prediction

scholarly journals Bayesian Prediction of Order Statistics Based on k-Record Values from a Generalized Exponential Distribution

Stats ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 447-456
Author(s):  
Zoran Vidović
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Record Values ◽  
Bayesian Prediction ◽  
Generalized Exponential Distribution ◽  
Lower Record ◽  
Lower Record Values ◽  
Future Sample

We examine in this paper the implementation of Bayesian point predictors of order statistics from a future sample based on the k th lower record values from a generalized exponential distribution.

Relationships between order statistics and record values and some characterization results

1984 ◽  
Vol 21 (02) ◽  
pp. 425-430 ◽  
Author(s):  
Ramesh C. Gupta
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Continuous Distribution ◽  
Record Values

The similarity between order statistics and record values motivated us, in this paper, to investigate some relationships between the order statistics and the record values. These relationships are employed to characterize a continuous distribution by some moment properties of the spacings of the record values, and hence obtain characterizations of the exponential distribution. Some of the well-known results follow trivially.

Concomitants of order statistics and record values from Bairamov-Kotz-Becki-FGM bivariate-generalized exponential distribution

Filomat ◽  
2018 ◽  
Vol 32 (9) ◽  
pp. 3313-3324 ◽  
Author(s):  
H.M. Barakat ◽  
E.M. Nigm ◽  
A.H. Syam
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Recurrence Relations ◽  
Record Values ◽  
Generalized Exponential Distribution ◽  
Bivariate Distributions ◽  
Concomitants Of Order Statistics ◽  
Distributional Properties

We introduce the Bairamov-Kotz-Becki-Farlie-Gumble-Morgenstern (BKB-FGM) type bivariategeneralized exponential distribution. Some distributional properties of concomitants of order statistics as well as record values for this family are studied. Recurrence relations between the moments of concomitants are obtained, some of these recurrence relations were not publishes before for Morgenstern type bivariate distributions. Moreover, most of the paper results are extended to arbitrary distributions (see Remark 3.1).

Relationships between order statistics and record values and some characterization results

1984 ◽  
Vol 21 (2) ◽  
pp. 425-430 ◽  
Author(s):  
Ramesh C. Gupta
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Continuous Distribution ◽  
Record Values

The similarity between order statistics and record values motivated us, in this paper, to investigate some relationships between the order statistics and the record values. These relationships are employed to characterize a continuous distribution by some moment properties of the spacings of the record values, and hence obtain characterizations of the exponential distribution. Some of the well-known results follow trivially.

The characterization of distributions by order statistics and record values — a unified approach

1984 ◽  
Vol 21 (2) ◽  
pp. 326-334 ◽  
Author(s):  
Paul Deheuvels
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Simple Proof ◽  
Order Statistic ◽  
Continuous Distribution ◽  
Record Values ◽  
Discrete Distributions ◽  
Unified Approach ◽  
Characterization Of Distributions

It is shown that, in some particular cases, it is equivalent to characterize a continuous distribution by properties of records and by properties of order statistics. As an application, we give a simple proof that if two successive jth record values and associated to an i.i.d. sequence are such that and are independent, then the sequence has to derive from an exponential distribution (in the continuous case). The equivalence breaks up for discrete distributions, for which we give a proof that the only distributions such that Xk, n and Xk+1, n – Xk, n are independent for some k ≧ 2 (where Xk, n is the kth order statistic of X1, ···, Xn) are degenerate.

The characterization of distributions by order statistics and record values — a unified approach

1984 ◽  
Vol 21 (02) ◽  
pp. 326-334
Author(s):  
Paul Deheuvels
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Simple Proof ◽  
Order Statistic ◽  
Continuous Distribution ◽  
Record Values ◽  
Discrete Distributions ◽  
Unified Approach ◽  
Characterization Of Distributions

It is shown that, in some particular cases, it is equivalent to characterize a continuous distribution by properties of records and by properties of order statistics. As an application, we give a simple proof that if two successivejth record valuesandassociated to an i.i.d. sequence are such thatandare independent, then the sequence has to derive from an exponential distribution (in the continuous case). The equivalence breaks up for discrete distributions, for which we give a proof that the only distributions such thatXk, nandXk+1,n–Xk, nare independent for somek≧ 2 (whereXk, nis thekth order statistic ofX1, ···,Xn) are degenerate.

scholarly journals Inference for generalized exponential distribution based on generalized order statistics

2015 ◽  
Vol 4 (2) ◽  
pp. 370
Author(s):  
Eldesoky Afify
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Survival Function ◽  
Simulated Data ◽  
Record Values ◽  
Generalized Order Statistics ◽  
Generalized Exponential Distribution ◽  
Upper Record Values ◽  
Upper Record ◽  
Generalized Order

<p>Estimation of a parameter of generalized exponential distribution (gexp) is obtained based on generalized order statistics. The maximum likelihood and Bayes methods are used for this purpose. Survival function and hazard rate are also computed. Estimation based on upper record values from generalized exponential distribution is obtained as a special case and compared by simulated data.</p>

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