Statistical Inference for Topp–Leone-generated Family of Distributions Based on Records

Arshad, Mohd Anuar; Azhad, Qazi J.

doi:10.2991/jsta.d.190306.008

Cited by 8 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where ( ) is the ML prediction for the future observation of ( ) , ( ) is given by (11), is defined in (15), ( ) is given by (18), , , * ( , , ) is defined in (33), ∫ * and * are given by (35).…”

Section: Balanced Linear Exponential Loss Functionmentioning

confidence: 99%

“…The one-sample Bayesian prediction and intervals of the generalized half-normal distribution under progressive Type-II censoring were studied by Abd El-Raheem [32]. Arshad and Jamal [33] predicted future record values using Bayesian approach of the TL family of distributions. Recently, Okasha et al [34] derived the Bayesian and E-Bayesian prediction (point and interval) based on observed order statistics with two samples from two parameter Burr XII model based on Type-II censored data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction for Modified Topp Leone-Chen Distribution Based on Progressive Type-II Censoring Scheme

AL-Dayian

EL-Helbawy

AL-Sayed

et al. 2021

JAMCS

View full text Add to dashboard Cite

Prediction of future events on the basis of the past and present information is a fundamental problem of statistics, arising in many contexts and producing varied solutions. The predictor can be either a point or an interval predictor. This paper focuses on predicting the future observations from the modified Topp-Leone Chen distribution based on progressive Type-II censored scheme. The two-sample prediction is applied to obtain the maximum likelihood, Bayesian and E-Bayesian prediction (point and interval) for future order statistics. The Bayesian and E-Bayesian predictors are considered based on two different loss functions, the balanced squared error loss function; as a symmetric loss function and balanced linear exponential loss function; as an asymmetric loss function. The predictors are obtained based on conjugate gamma prior and uniform hyperprior distributions. A numerical example is provided to illustrate the theoretical results and an application using real data sets are used to demonstrate how the results can be used in practice.

show abstract

Section: Balanced Linear Exponential Loss Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction for Modified Topp Leone-Chen Distribution Based on Progressive Type-II Censoring Scheme

AL-Dayian

EL-Helbawy

AL-Sayed

et al. 2021

JAMCS

View full text Add to dashboard Cite

show abstract

“…Prediction has been applied in a variety of disciplines such as medicine, engineering, business, economic and other areas as well. Prediction for order statistics of future observables from certain distributions has been studied by several authors, such as, Valiollahi et al (2017), Faizan and Sana (2018), Arshad and Jamal (2019), Okasha et al (2020), Ahmad (2021) and AL-Dayian et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation and Prediction for Marshall-Olkin Weibull-Exponential Distribution Based on Progressive Type-II Censoring Scheme

AL-Sayed

Behairy

2023

التجارة والتمويل

View full text Add to dashboard Cite

Lifetime distributions under progressive Type-II censored scheme have been attracting great interest due to their wide application in the fields of science, engineering, social sciences and medicine. Also, prediction of future events on the basis of the past and present knowledge without any doubt is one of the most important problems in statistics. In this paper, the Bayes estimators for the parameters of the Marshall-Olkin Weibull-exponential distribution are derived based on progressive Type-II censored scheme. The estimators are considered under two different loss functions, the balanced squared error loss function; as a symmetric loss function and the balanced linear exponential loss function; as an asymmetric loss function. Also, the two-sample prediction method is applied to obtain the Bayesian prediction (point and interval) for future order statistics. A numerical example is provided to illustrate the theoretical results and an application using real data set is used to demonstrate how the results can be used in practice.

show abstract

“…On the other side, in the recent years, the Topp-Leone distribution reveals to be particularly efficient to define general families of distributions enjoying nice properties, including a great ability to model different practical data sets. Among these families, there are the Topp-Leone-G family studied via different approaches by [11][12][13][14], the Topp-Leone-G power series family by [15,16], the type II Topp-Leone-G family by [17], the Topp-Leone odd log-logistic family by [18], the type II generalized Topp-Leone-G family by [19], the Fréchet Topp-Leone-G family by [20], the exponentiated generalized Topp-Leone-G family by [21] and the transmuted Topp-Leone-G family by [22]. Now, for the purposes of this paper, let us describe the general family introduced by [23].…”

Section: Introductionmentioning

confidence: 99%

Type II Power Topp-Leone Generated Family of Distributions with Statistical Inference and Applications

Bantan

Jamal²,

Chesneau

et al. 2020

Symmetry

View full text Add to dashboard Cite

In this paper, we present and study a new family of continuous distributions, called the type II power Topp-Leone-G family. It provides a natural extension of the so-called type II Topp-Leone-G family, thanks to the use of an additional shape parameter. We determine the main properties of the new family, showing how they depend on the involving parameters. The following points are investigated: shapes and asymptotes of some important functions, quantile function, some mixture representations, moments and derivations, stochastic ordering, reliability and order statistics. Then, a special model of the family based on the inverse exponential distribution is introduced. It is of particular interest because the related probability functions are tractable and possess various kinds of asymmetric shapes. Specially, reverse J, left skewed, near symmetrical and right skewed shapes are observed for the corresponding probability density function. The estimation of the model parameters is performed by the use of three different methods. A complete simulation study is proposed to illustrate their numerical efficiency. The considered model is also applied to analyze two different kinds of data sets. We show that it outperforms other well-known models defined with the same baseline distribution, proving its high level of adaptability in the context of data analysis.

show abstract

Statistical Inference for Topp–Leone-generated Family of Distributions Based on Records

Cited by 8 publications

References 23 publications

Prediction for Modified Topp Leone-Chen Distribution Based on Progressive Type-II Censoring Scheme

Prediction for Modified Topp Leone-Chen Distribution Based on Progressive Type-II Censoring Scheme

Bayesian Estimation and Prediction for Marshall-Olkin Weibull-Exponential Distribution Based on Progressive Type-II Censoring Scheme

Type II Power Topp-Leone Generated Family of Distributions with Statistical Inference and Applications

Contact Info

Product

Resources

About