A New Four-ParameterWeibull Model for Lifetime Data

Yousof, Haitham M.; Afify, Ahmed Z.; Cordeiro, Gauss M.; Alzaatreh, Ayman; Ahsanullah, Mohammad

doi:10.2991/jsta.2017.16.4.3

Cited by 17 publications

(11 citation statements)

References 19 publications

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“…Here, we shall compare the fits of the OBBX distribution with those of other competitive models, namely, the BX [1], odd Lindley exponentiated W (OLEW) [24], Burr X EW (BXEW) [22], Poisson Topp Leone W (PTLW) [25], Marshall Olkin extended-W (MOEW) [26], gamma-W (GamW) [27], Kumaraswamy-W (KumW) [28], beta-W [29], transmuted modified-W (TrMW) [30], modified beta-W (MBW) [31], Mcdonald-W (MacW) [32], and transmuted exponentiated generalized W (TrEGW) [33] distributions. Some other extensions of the W distribution can also be used in this comparison, but are not limited to [34][35][36][37][38][39][40][41][42][43]. Figure 9 presents the TTT, box, Q-Q, and NKDE plots for data set I.…”

Section: Modeling Failure Timesmentioning

confidence: 99%

A New Bimodal Distribution for Modeling Asymmetric Bimodal Heavy-Tail Real Lifetime Data

Butt¹,

Khalil

2020

Symmetry

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We introduced and studied a new generalization of the Burr type X distribution. Some of its properties were derived and numerically analyzed. The new density can be “right-skewed” and symmetric with “unimodal” and many “bimodal” shapes. The new failure rate can be “increasing,” “bathtub,” “J-shape,” “decreasing,” “increasing-constant-increasing,” “reversed J-shape,” and “upside-down (reversed U-shape).” The usefulness and flexibility of the new distribution were illustrated by means of four asymmetric bimodal right- and left-heavy tail real lifetime data.

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Section: Modeling Failure Timesmentioning

confidence: 99%

A New Bimodal Distribution for Modeling Asymmetric Bimodal Heavy-Tail Real Lifetime Data

Butt¹,

Khalil

2020

Symmetry

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“…Staying in (1) and for 𝑏 = 2, the GW-G reduces to generalized Rayleigh G (ER-G) (Yousof et al (2017a)) which is also called the Burr X (BX-G). Let 𝑌 𝒾 denote the failure time of the ith subsystem and let 𝑍 = 𝑚𝒾𝑛{𝑌 1 , 𝑌 2 , ⋯ , 𝑌 𝑁 }.…”

Section: Introductionmentioning

confidence: 99%

A Novel Generator of Continuous Probability Distributions for the Asymmetric Left-skewed Bimodal Real-life Data with Properties and Copulas

Shehata

Yousof

Aboraya

2021

Pak.j.stat.oper.res.

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This paper presents a novel two-parameter G family of distributions. Relevant statistical properties such as the ordinary moments, incomplete moments and moment generating function are derived. Using common copulas, some new bivariate type G families are derived. Special attention is devoted to the standard exponential base line model. The density of the new exponential extension can be “asymmetric and right skewed shape” with no peak, “asymmetric right skewed shape” with one peak, “symmetric shape” and “asymmetric left skewed shape” with one peak. The hazard rate of the new exponential distribution can be “increasing”, “U-shape”, “decreasing” and “J-shape”. The usefulness and flexibility of the new family is illustrated by means of two applications to real data sets. The new family is compared with many common G families in modeling relief times and survival times data sets.

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“…A state-of-the-art survey on the class of such distributions can be found in ([ 1 ]). Some extensions of the W distribution with more than two parameters are available in the literature, such as exponentiated W (Exp-W) ([ 2 , 3 ]), the additive W ([ 4 ]), the Marshall–Olkin extended W ([ 5 ]), the beta inverse W ([ 6 ]), transmuted exponentiated generatized W ([ 7 ]), Marshall–Olkin additive W ([ 8 ]), the Topp Leone generated W distribution ([ 9 ]), the exponentiated generalized W Poisson ([ 10 ]), Type I general exponential W ([ 11 ]), new four-parameter W ([ 12 ]), Burr XII W ([ 13 ]), Marshall–Olkin generalized W Poisson ([ 14 ]), odd Lindley W ([ 15 ]), Lindley W ([ 16 ]), W generalized W ([ 17 ]), new extended W ([ 18 ]), Type II general exponential W ([ 19 ]), Burr X exponentiated W ([ 20 ]), odd power Lindley W ([ 21 ]), odd Nadarajah-Haghighi W ([ 22 ]), and WW Poisson ([ 23 ]).…”

Section: Introductionmentioning

confidence: 99%

A New Parametric Life Distribution with Modified Bagdonavičius–Nikulin Goodness-of-Fit Test for Censored Validation, Properties, Applications, and Different Estimation Methods

Mansour

Rasekhi

Ibrahim

et al. 2020

Entropy

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In this paper, we first study a new two parameter lifetime distribution. This distribution includes “monotone” and “non-monotone” hazard rate functions which are useful in lifetime data analysis and reliability. Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, Renyi entropy, δ-entropy, order statistics and probability weighted moments are derived. Non-Bayesian estimation methods such as the maximum likelihood, Cramer-Von-Mises, percentile estimation, and L-moments are used for estimating the model parameters. The importance and flexibility of the new distribution are illustrated by means of two applications to real data sets. Using the approach of the Bagdonavicius–Nikulin goodness-of-fit test for the right censored validation, we then propose and apply a modified chi-square goodness-of-fit test for the Burr X Weibull model. The modified goodness-of-fit statistics test is applied for the right censored real data set. Based on the censored maximum likelihood estimators on initial data, the modified goodness-of-fit test recovers the loss in information while the grouped data follows the chi-square distribution. The elements of the modified criteria tests are derived. A real data application is for validation under the uncensored scheme.

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A New Four-ParameterWeibull Model for Lifetime Data

Cited by 17 publications

References 19 publications

A New Bimodal Distribution for Modeling Asymmetric Bimodal Heavy-Tail Real Lifetime Data

A New Bimodal Distribution for Modeling Asymmetric Bimodal Heavy-Tail Real Lifetime Data

A Novel Generator of Continuous Probability Distributions for the Asymmetric Left-skewed Bimodal Real-life Data with Properties and Copulas

A New Parametric Life Distribution with Modified Bagdonavičius–Nikulin Goodness-of-Fit Test for Censored Validation, Properties, Applications, and Different Estimation Methods

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