Dual Generalized Order Statistics Research Articles

A New Inverse Kumaraswamy Family of Distributions: Properties and Application

The Kumaraswamy distribution is an important probability distribution used to model several hydrological problems as well as various natural phenomena whose process values are bounded on both sides. In this paper, we introduce a new family of inverse Kumaraswamy distribution and then explore its statistical properties. Conventional maximum likelihood estimators are considered for the parameters of this distribution and estimation based on dual generalized order statistics is outlined. A particular sub-model of this family; namely, the inverse Kumaraswamy- Weibull distribution is considered and some of its statistical properties are obtained. Estimation efficiency is numerically evaluated via a simulation study and two real-data applications of the proposed distribution are provided as well.

On recurrence relations for moments of dual generalized order statistics for a general transmuted power function distributions with characterizations

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.Cover :Leatherback (a), green (b - adult & f - hatchling), hawksbill (c) and olive ridley (d) turtles who nest in Sri Lankan beaches, green turtle crawl marks (e) and by-catch of sea turtles in fisheries (c & d)Photo credits: SJ Perera (a & c), WPN Perera (b & e), K Ekaratne (d & f)

Moments of Dual Generalized Order Statistics and Characterization for Transmuted Exponential Model

Moments of Dual Generalized Order Statistics and Characterization for Transmuted Exponential Model

Dual generalized order statistics with moments properties using powered inverse Rayleigh distribution

The basic concept of ordered random variables is how the ordering is being done for a model. Dual generalized order statistics (dgos) is one of them. The powered inverse Rayleigh distribution is introduced in this research investigation to analyze the moments’ properties based on dgos. This introduced distribution was coined by Nashaat [1]. The characterization findings based on the recurrence relations and conditional moments are also derived. In addition, some statistical calculations are performed.

Analysis of an Inverted Modified Lindley Distribution Using Dual Generalized Order Statistics

Analysis of an Inverted Modified Lindley Distribution Using Dual Generalized Order Statistics

Moment Properties of Dual Generalized Order Statistics Based on Fre ́chet-Weibull Distribution

Moment Properties of Dual Generalized Order Statistics Based on Fre ́chet-Weibull Distribution

Predictive inference of dual generalized order statistics from the inverse Weibull distribution

In this paper, some predictive results of dual generalized order statistics (DGOSs) from the inverse Weibull distribution are obtained. For this goal, different predictive and reconstructive pivotal quantities are proposed. Moreover, several predictive and reconstructive intervals concerning DGOSs based on the inverse Weibull distribution are constructed. Furthermore, the maximum likelihood predictor as well as the predictive maximum likelihood estimates based on DGOSs are studied. Finally, simulation studies are carried out to assess the efficiency of the obtained results.

On concomitants of generalized order statistics from generalized FGM family under a general setting

AbstractIn this work, we study the distributional and moment properties of concomitants of dual generalized order statistics and consequently generalized order statistics. When the parameters are assumed to be pairwise different from Bairamov-Kotz-Becki Farlie-Gumbel-Morgenstern (BKB-FGM) family. Furthermore, an example of progressive type-II censored order statistics of BKB-FGM family under uniform distribution is obtained. Moreover, the joint distribution of dual generalized order statistics and record values of concomitants for this family are discussed. Finally, an application is given for some well-known distributions such as exponential, Pareto, and power function distributions.

Some Results on Exponentiated Weibull Distribution via Dual Generalized Order Statistics

In this paper, we use the concept of dual generalized order statistics dgos which was given by Pawlas and Syznal (2001). By using this, we obtain the various theorems and some relations through ratio and inverse moment by using exponentiated-Weibull distribution. Cases for order statistics and lower record values are also considered. Further, we characterize the exponentiated-Weibull distribution through three different methods by using the results obtained in this paper.

Some Characterizations of the Exponential Distribution by Generalized Order Statistics, with Applications to Statistical Prediction Problem

Some new characterization properties of the exponential distribution based on two non-adjacent m-generalized order statistics (consequently m dual generalized order statistics), m ̸= −1, coming from two independent exponential distributions are derived. The result of this paper provides a beneficial strategy to predict the failure time of some survived components in a lifetime experiment by using the result of another independent lifetime experiment.

A Study on Moments of Dual Generalized Order Statistics from Exponentiated Generalized Class of Distributions

In this paper, relations between moments of dual generalized order statistics from an exponentiated generalized class of distributions, given by Cardeiro (2013) are studied. Some particular cases of dual generalized order statistics and examples based on it are discussed. The characterization of given distribution based on moment properties is also presented.

Inferences for generalized Topp-Leone distribution under dual generalized order statistics with applications to Engineering and COVID-19 data

This article accentuates the estimation of a two-parameter generalized Topp-Leone distribution using dual generalized order statistics (dgos). In the part of estimation, we obtain maximum likelihood (ML) estimates and approximate confidence intervals of the model parameters using dgos, in particular, based on order statistics and lower record values. The Bayes estimate is derived with respect to a squared error loss function using gamma priors. The highest posterior density credible interval is computed based on the MH algorithm. Furthermore, the explicit expressions for single and product moments of dgos from this distribution are also derived. Based on order statistics and lower records, a simulation study is carried out to check the efficiency of these estimators. Two real life data sets, one is for order statistics and another is for lower record values have been analyzed to demonstrate how the proposed methods may work in practice.

Bayesian inference of Unit Gompertz distribution based on dual generalized order statistics

In this article, we consider the estimation problem of Unit Gompertz distribution with parameters α and β under the framework of dual generalized order statistics. This article is purely devoted to present the Bayesian view of estimation of Unit Gompertz distribution. For this purpose, we consider two widely popular approximation methods called Markov chain Monte Carlo and Lindley approximation methods. The results are derived under the symmetric (squared error) and asymmetric (Linear exponential and General entropy) loss functions. Since the order statistics and lower record values are the particular cases of the dual generalized order statistics, a simulation study is provided for order statistics and lower record values to observe the behavior of estimators. The average lengths of highest posterior density intervals of α, β, and R(t) are calculated for 95% confidence coefficient. Finally, real data applications are reported for lower record values and order statistics, separately, to show the practical aspects of the derived results.

Moments of Dual Generalized Order Statistics from Topp Leone Weighted Weibull Distribution and Characterization

Moments of Dual Generalized Order Statistics from Topp Leone Weighted Weibull Distribution and Characterization

Estimation for Exponentiated Generalized Xgamma Distribution from New General Class Based on Dual Generalized Order Statistics

Estimation for Exponentiated Generalized Xgamma Distribution from New General Class Based on Dual Generalized Order Statistics

Bayesian Prediction Based on Dual Generalized Order Statistics Using Multiply Type-II Censoring

Bayesian Prediction Based on Dual Generalized Order Statistics Using Multiply Type-II Censoring

Information Measures for Generalized Order Statistics and Their Concomitants under General Framework from Huang-Kotz FGM Bivariate Distribution.

In this paper, we study the concomitants of dual generalized order statistics (and consequently generalized order statistics) when the parameters are assumed to be pairwise different from Huang–Kotz Farlie–Gumble–Morgenstern bivariate distribution. Some useful recurrence relations between single and product moments of concomitants are obtained. Moreover, Shannon’s entropy and the Fisher information number measures are derived. Finally, these measures are extensively studied for some well-known distributions such as exponential, Pareto and power distributions. The main motivation of the study of the concomitants of generalized order statistics (as an important practical kind to order the bivariate data) under this general framework is to enable researchers in different fields of statistics to use some of the important models contained in these generalized order statistics only under this general framework. These extended models are frequently used in the reliability theory, such as the progressive type-II censored order statistics.

On concomitants of dual generalized order statistics from Bairamov–Kotz–Becki Farlie–Gumbel–Morgenstern bivariate distributions

In this paper, we study the concomitants of [Formula: see text]-dual generalized order statistics ([Formula: see text]-DGOS) from Bairamov–Kotz–Becki Farlie–Gumbel–Morgenstern bivariate distributions as an extension of several recent papers. This study can also be applied to the model of [Formula: see text]-generalized order statistics ([Formula: see text]-GOS) as a parallel model of [Formula: see text]-DGOS. Furthermore, the joint distribution of [Formula: see text]-DGOS of concomitants for this family is studied. Some useful recurrence relations between single and product moments of concomitants are obtained. Moreover, most of the paper results are extended to any arbitrary distribution. Finally, an application of these results is given for bivariate generalized exponential distribution.

Moments of Dual Generalized Order Statistics from Inverted Kumaraswamy Distribution and Related Inference

In this paper, the exact and explicit expressions for single, product, and conditional moments for inverted Kumaraswamy distribution using dual generalized order statistics (dgos) are derived. Further, the expression for maximum likelihood estimator (MLE) and uniformly minimum variance unbiased estimator (UMVUE) for the parameters of inverted Kumaraswamy distribution based on dgos are deduced. Also, we obtained the results for order statistics and lower record values by putting some specific values of the parameters of dgos. Finally, a simulation study is carried out for illustrative purpose.

Moments of Dual Generalized Order Statistics from Extended Erlang-Truncated Exponential Distribution

Moments of Dual Generalized Order Statistics from Extended Erlang-Truncated Exponential Distribution