Order statistics for discrete case with a numerical application to the binomial distribution

1956 ◽  
Vol 8 (2) ◽  
pp. 95-104 ◽  
Author(s):  
Minoru Siotani
Keyword(s):  
Order Statistics ◽  
Binomial Distribution ◽  
Discrete Case ◽  
Numerical Application

scholarly journals A Note on the Net Premium for a Generalized Largest Claims Reinsurance Cover

1998 ◽  
Vol 28 (1) ◽  
pp. 153-162 ◽  
Author(s):  
Raoul M. Berglund
Keyword(s):  
Order Statistics ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Mutually Independent

AbstractIn the present paper the author gives net premium formulae for a generalized largest claims reinsurance cover. If the claim sizes are mutually independent and identically 3-parametric Pareto distributed and the number of claims has a Poisson, binomial or negative binomial distribution, formulae are given from which numerical values can easily be obtained. The results are based on identities for compounded order statistics.

scholarly journals On order statistics from nonidentical discrete random variables

Open Physics ◽  
2016 ◽  
Vol 14 (1) ◽  
pp. 192-196
Author(s):  
Bahadır Yüzbaşı ◽  
Yunus Bulut ◽  
Mehmet Güngör
Keyword(s):  
Order Statistics ◽  
Order Statistic ◽  
Random Variables ◽  
Integral Form ◽  
Discrete Case ◽  
Discrete Random Variables

AbstractIn this study, pf and df of single order statistic of nonidentical discrete random variables are obtained. These functions are also expressed in integral form. Finally, pf and df of extreme of order statistics of random variables for the nonidentical discrete case are given.

scholarly journals A New Neutrosophic Negative Binomial Distribution: Properties and Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Rehan Ahmad Khan Sherwani ◽  
Sadia Iqbal ◽  
Shumaila Abbas ◽  
Muhammad Aslam ◽  
Ali Hussein AL-Marshadi
Keyword(s):  
Probability Distribution ◽  
Order Statistics ◽  
Generating Function ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Hazard Rate ◽  
Negative Binomial ◽  
Real Life ◽  
Probability Generating Function ◽  
Interval Valued

Many problems in real life exist that are full of confusion, vagueness, and ambiguity. The quantification of such issues in a scientific way is the need of time. The negative binomial distribution is an important discrete probability distribution from the account of classical probability distribution theory. The distribution was used to study the chance of kth success in n trials before n − 1 failures for crisp data. The literature lacks in dealing with the situations for interval-valued data under negative binomial distribution. In this research, the neutrosophic negative binomial distribution is proposed to generalize the classical negative binomial distribution. The generalized proposed distribution considers the indeterminacy and crisp form from interval-valued. Several properties of the proposed distribution, such as moment generating function, characteristic function, and probability generating function, are also derived. Furthermore, the derivation of reliability analysis properties such as survival, hazard rate, reversed hazard rate, cumulative hazard rate, mills ratio, and odds ratio are also presented. In addition, order statistics for the proposed distribution, including w th , joint, median, minimum, and maximum order statistics are part of the paper. The proposed distribution is discussed from the real data applications perspective by considering the different case studies. This research opens the way to deal with the problems that follow conventional conveyances and include nonprecisely determined details simultaneously.

scholarly journals On joint distributions of order statistics for a discrete case

2012 ◽  
Vol 2012 (1) ◽  
Author(s):  
M Güngör ◽  
Y Bulut ◽  
B Yüzbaşı
Keyword(s):  
Order Statistics ◽  
Discrete Case ◽  
Joint Distributions

An Urn Model and the Prediction of Order Statistics

1975 ◽  
Vol 4 (3) ◽  
pp. 245-250
Author(s):  
Kenneth Kaminsky ◽  
Eugene Luks ◽  
Paul Nelson
Keyword(s):  
Order Statistics ◽  
Urn Model
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