The inverse Weibull inverse exponential distribution with application

Aldahlan, Maha A.

doi:10.12988/ijcms.2019.913

Cited by 14 publications

(10 citation statements)

References 7 publications

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“…Lemma 4. Let ðU, VÞ be a BIEGIKw-Weibull random variable with pdf and marginals given in ( 4), (7), and (8). Then, the copula density function is given by…”

Section: Marginals and Momentsmentioning

confidence: 99%

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A New Bivariate Extended Generalized Inverted Kumaraswamy Weibull Distribution

Ragab

Elhassanein

2022

Advances in Mathematical Physics

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This article presents a new bivariate extended generalized inverted Kumaraswamy Weibull (BIEGIKw-Weibull) distribution with nine parameters. Statistical properties of the new distribution are discussed. Forms of copulas, moments, conditional moments, bivariate reliability function, and bivariate hazard rate function are derived. Maximum likelihood estimators are formulated. Simulation is conducted for three different sets of parameters to verify the theoretical results and to discuss the new distribution properties. The performance of the maximum likelihood method is investigated via Monte Carlo simulation depending on the bias and the standard error. Simulated lifetime data is used as an application of the new model.

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“…Lemma 4. Let ðU, VÞ be a BIEGIKw-Weibull random variable with pdf and marginals given in ( 4), (7), and (8). Then, the copula density function is given by…”

Section: Marginals and Momentsmentioning

confidence: 99%

“…The inverse Weibull (IW) distribution is widely used because of its applicability in various fields, like medicine, statistics, engineering, physics, and fluid mechanics [1][2][3][4][5][6][7][8][9][10][11]. To enhance such distributions, researchers introduced new generators by supplementing shape parameters to the base line distribution.…”

Section: Introductionmentioning

confidence: 99%

A New Bivariate Extended Generalized Inverted Kumaraswamy Weibull Distribution

Ragab

Elhassanein

2022

Advances in Mathematical Physics

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“…We consider the TIIPTLIEx model and all the consider model parameters will be estimated by the maximum likelihood method, with the use of the R software. We compare the TIIPTLIEx model with seven three(or less)-parameter models connected to the IEx model, namely: the Kumaraswamy inverse exponential (KIEx) model (see [31]), beta inverse exponential (BIEx) model (see [32]) by keeping shape parameter is equal to one, alpha-power inverse Weibull (AIW) model (see [33]), logistic inverse exponential (LIEx) model (see [34]), inverse Weibull inverse exponential (IWIEx) model (see [35]), type II Topp-Leone generalized inverse Rayleigh (TIR) model (see [36]) and standard IEX model.…”

Section: Applicationsmentioning

confidence: 99%

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

Bantan

Jamal²,

Chesneau

et al. 2020

Symmetry

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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.

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“…We compare the fits of the TIITFIE model with some known models like; the beta IE (BIE) model (see Khan [6]) by keeping shape parameter is equal to one, logistic IE (LIE) model (see Oguntunde et al [8]), inverse Weibull inverse exponential (IWIE) model (see Aldahlan [3]), and standard IE model. We estimate the model Parameterss by using ML method.…”

Section: Applicationmentioning

confidence: 99%

Estimation of type II truncated Fr'echet inverse exponential distribution under censored data

Aldahlan¹

2020

J. Nonlinear Sci. Appl.

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This paper proposes a new probability distribution called as type II truncated Fréchet inverse exponential distribution. Fundamental statistical properties like moments, incomplete moments, and quantile function of the proposed model are studied. The estimation of the parameter of the new model is approached by maximum likelihood method based on complete and censored samples. Asymptotic confidence interval of model parameters is investigated. We assess the numerical results to study the theoretical results. Superiority of the type II truncated Fréchet inverse exponential model over some known distributions is showed through one real data set.

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The inverse Weibull inverse exponential distribution with application

Cited by 14 publications

References 7 publications

A New Bivariate Extended Generalized Inverted Kumaraswamy Weibull Distribution

A New Bivariate Extended Generalized Inverted Kumaraswamy Weibull Distribution

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

Estimation of type II truncated Fr'echet inverse exponential distribution under censored data

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