On distributions Of generalized order statistics

Kamps, Udo; Cramer, Erhard

doi:10.1080/02331880108802736

Cited by 207 publications

(98 citation statements)

References 9 publications

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“…This result can be directly derived from the corresponding recurrence relation between the cdf's of OS. Kamps and Cramr Lemma 4 [12] obtained the corresponding recurrence relation for generalized OS as…”

Section: Recurrence Relationsmentioning

confidence: 99%

The Entropy of Progressively Censored Samples

Abo-Eleneen

2011

Entropy

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In many life-testing and reliability studies, the experimenter might not always obtain complete information on failure times for all experimental units. Among the different censoring schemes, the progressive censoring scheme has received a considerable attention in the last few years. The aim of this paper is simplifying the entropy of progressively Type II censored samples. We propose an indirect approach using a decomposition of the entropy in progressively Type II censored samples to simplify the calculation. Some recurrence relations for the entropy in progressively Type II censored samples are derived to facilitate this calculation. An efficient computational method is derived that simplifies computation of the entropy in progressively Type II censored samples to a sum; entropy in collections order statistics. We compute the entropy in a collection of progressively Type II censored samples for some known distributions.

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Section: Recurrence Relationsmentioning

confidence: 99%

The Entropy of Progressively Censored Samples

Abo-Eleneen

2011

Entropy

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“…Moreover, let c r−1 = ∏ r j=1 γ j , r = 1, ..., n − 1, and γ n = k. Here, we will assume throughout that the parameters γ 1 , ..., γ n are pairwise different (see, Kamps and Cramer [11]) i.e.,…”

Section: Introductionmentioning

confidence: 99%

“…For 1 ≤ r < s ≤ n, the marginal density function of X(r, n,m, k) and joint density function of X(s, n,m, k) and X(r, n,m, k) is respectively given by (see Kamps and Cramer, [11] )…”

Section: Introductionmentioning

confidence: 99%

Characterizing Distributions by Linearity of Regression of Generalized Order Statistics

Rasouli¹,

Samadi²,

Ahsanullah³

2016

JSTA

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“…For more generalization, this article discusses the predicting of future generalized order statistics based on generalized order statistics. The study was conducted over all assumptions of generalized order statistics (gOSs) [12] and [13]. Paper is organized as follows: In section 2, some preliminaries are given.…”

Section: Introductionmentioning

confidence: 99%

“…, n − 1 and i = j, the pdf of X r,n, m,k which is given in Eq. (1) as be introduced by Kamps and Cramer [13], is given by:…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Prediction Intervals of generalized order statistics from two independent sequences

El-Din

Emam

2016

IJASP

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This paper, discusses the problem of predicting future a generalized order statistic of an iid sequence sample was drawn from an arbitrary unknown distribution, based on observed also generalized order statistics from the same population. The coverage probabilities of these prediction intervals are exact and free of the parent distribution F(). Prediction formulas of ordinary order statistics and upper record values are extracted as special cases from the productive results. Finally, numerical computations on several models of ordered random variables are given to illustrate the proposed procedures.

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On distributions Of generalized order statistics

Cited by 207 publications

References 9 publications

The Entropy of Progressively Censored Samples

The Entropy of Progressively Censored Samples

Characterizing Distributions by Linearity of Regression of Generalized Order Statistics

Nonparametric Prediction Intervals of generalized order statistics from two independent sequences

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