Characterization of a New Family of Distribution Through Upper Record Values

2021 ◽  
Vol 52 ◽  
Author(s):  
Md. Izhar Khan
Keyword(s):  
Conditional Expectation ◽  
Record Values ◽  
New Class ◽  
New Family ◽  
Upper Record Values ◽  
Record Value ◽  
Upper Record

In this paper, a new class of distribution has been characterized through the condi- tional expectations, conditioned on a non-adjacent upper record value. Also an equivalence between the unconditional and conditional expectation is used to characterize the new class of distribution.

Record values and extreme value distributions

1982 ◽  
Vol 19 (01) ◽  
pp. 233-239 ◽  
Author(s):  
H. N. Nagaraja
Keyword(s):  
Random Variable ◽  
Record Values ◽  
Canonical Representation ◽  
Extreme Value ◽  
Extreme Value Distributions ◽  
Upper Record Values ◽  
Lower Record ◽  
Record Value ◽  
Upper Record

The limit distribution of thekth maximum from a random sample of sizenwhenn →∞ is identified as the distribution of thekth lower record value from one of three extreme value distributions. This fact is used in giving a different canonical representation and new proofs of the results of Hall (1978) for this limiting random variable. A characterization of the exponential distribution based on upper record values is given.

Record values and extreme value distributions

1982 ◽  
Vol 19 (1) ◽  
pp. 233-239 ◽  
Author(s):  
H. N. Nagaraja
Keyword(s):  
Random Variable ◽  
Record Values ◽  
Canonical Representation ◽  
Extreme Value ◽  
Extreme Value Distributions ◽  
Upper Record Values ◽  
Lower Record ◽  
Record Value ◽  
Upper Record

The limit distribution of the k th maximum from a random sample of size n when n → ∞ is identified as the distribution of the k th lower record value from one of three extreme value distributions. This fact is used in giving a different canonical representation and new proofs of the results of Hall (1978) for this limiting random variable. A characterization of the exponential distribution based on upper record values is given.

Inference about Weibull Distribution Using Upper Record Values

2015 ◽  
Vol 22 (04) ◽  
pp. 1550016
Author(s):  
Kai Huang ◽  
Jie Mi
Keyword(s):  
Weibull Distribution ◽  
Prediction Interval ◽  
Record Values ◽  
Testing Procedures ◽  
Scale Parameters ◽  
Upper Record Values ◽  
Record Value ◽  
Upper Record ◽  
Two Parameters ◽  
Two Parameter

This paper studies the frequentist inference about the shape and scale parameters of the two-parameter Weibull distribution using upper record values. The exact sampling distribution of the MLE of the shape parameter is derived. The asymptotic normality of the MLEs of both parameters are obtained. Based on these results this paper proposes various confidence intervals of the two parameters. Assuming one parameter is known certain testing procedures are proposed. Furthermore, approximate prediction interval for the immediately consequent record value is derived too. Conclusions are made based on intensive simulations.

scholarly journals Upper Record Values from Extended Exponential Distribution

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Devendra Kumar ◽  
Sanku Dey
Keyword(s):  
Exponential Distribution ◽  
Recurrence Relations ◽  
Shape Parameter ◽  
Record Values ◽  
Exponential Distributions ◽  
Product Moments ◽  
Conditional Moments ◽  
Upper Record Values ◽  
Upper Record

Some recurrence relations are established for the single and product moments of upper record values for the extended exponential distribution by Nadarajah and Haghighi (2011) as an alternative to the gamma, Weibull, and the exponentiated exponential distributions. Recurrence relations for negative moments and quotient moments of upper record values are also obtained. Using relations of single moments and product moments, means, variances, and covariances of upper record values from samples of sizes up to 10 are tabulated for various values of the shape parameter and scale parameter. A characterization of this distribution based on conditional moments of record values is presented.

scholarly journals The Odd Lindley-G Family of Distributions

2017 ◽  
Vol 46 (1) ◽  
pp. 65-87 ◽  
Author(s):  
Frank S. Gomes-Silva ◽  
Ana Percontini ◽  
Edleide de Brito ◽  
Manoel W. Ramos ◽  
Ronaldo Venâncio ◽  
...  
Keyword(s):  
Real Data ◽  
Quantile Function ◽  
Record Values ◽  
Model Parameters ◽  
Baseline Model ◽  
Data Set ◽  
New Family ◽  
Special Cases ◽  
Upper Record Values ◽  
Upper Record

We propose a new generator of continuous distributions with one extra positive parameter called the odd Lindley-G family. Some special cases are presented. The new density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. Various structural properties of the new family, which hold for any baseline model, are derived including explicit expressions for the quantile function, ordinary and incomplete moments, generating function, Renyi entropy, reliability, order statistics and their moments and k upper record values. We discuss estimation of the model parameters by maximum likelihood and provide an application to a real data set.

scholarly journals Some Characterizations of the Distribution of the Condition Number of a Complex Gaussian Matrix

2020 ◽  
Vol 8 (1) ◽  
pp. 22-35
Author(s):  
M. Shakil ◽  
M. Ahsanullah
Keyword(s):  
Order Statistics ◽  
Condition Number ◽  
Record Values ◽  
Distributional Properties ◽  
Upper Record Values ◽  
Upper Record

AbstractThe objective of this paper is to characterize the distribution of the condition number of a complex Gaussian matrix. Several new distributional properties of the distribution of the condition number of a complex Gaussian matrix are given. Based on such distributional properties, some characterizations of the distribution are given by truncated moment, order statistics and upper record values.

scholarly journals Nonparametric Prediction Intervals of generalized order statistics from two independent sequences

2016 ◽  
Vol 4 (1) ◽  
pp. 36 ◽  
Author(s):  
Mostafa Mohie El-Din ◽  
Walid Emam
Keyword(s):  
Order Statistics ◽  
Record Values ◽  
Prediction Intervals ◽  
Generalized Order Statistics ◽  
Special Cases ◽  
Upper Record Values ◽  
Upper Record ◽  
Nonparametric Prediction ◽  
Nonparametric Prediction Intervals ◽  
Generalized Order

<p>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.</p>

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