On Weighted Ailamujia Distribution and Its Applications to Lifetime Data

Jan, Uzma; Fatima, Kawsar; Ahmad, Sajjad

doi:10.18576/jsap/060317

Cited by 12 publications

(7 citation statements)

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“…For detailed properties and applications of the Эрланга distribution, we refer to Lv et al [1], Pan et al [15], Long [16] and Li [17]. For the weighted Эрланга distribution and its applications, we refer to Jan et al [18] and references therein.…”

Section: Size-biased (Weighted) эрланга Distributionmentioning

confidence: 99%

Untitled

2021

J. Stat. Appl. Pro.

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Section: Size-biased (Weighted) эрланга Distributionmentioning

confidence: 99%

Untitled

2021

J. Stat. Appl. Pro.

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“…In improving the flexibility of the distribution, different generalized forms have been proposed. The Weighted Ailamujia distribution was introduced by [8] while [9] proposed its Size-biased form. Also, [10] proposed the Marshall-Olkin extension; [7] introduced the Power Ailamujia distribution and [11] proposed the Odd-Weibull generalized version.…”

Section: Introductionmentioning

confidence: 99%

Transmuted Ailamujia Distribution with Applications to Lifetime Observations

Adetunji

2023

AJPAS

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Background of the Study: Quest to extend existing probability distributions to allow for more flexibility in data modelling is never ending. Aims: This study extends the Ailamujia distribution using quadratic transmutation map to propose a new 2-paramter lifetime distribution. Methodology: Shapes of the density function and the hazard rate function of the new propositions are obtained. Some mathematical properties of the new distribution are also obtained. Results: Application of the distribution to four lifetime datasets reveals that the distribution competes favourably with other related distributions. Conclusion: The new distribution competes favourably with the existing distributions in data modelling.

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“…Fatima and Ahmad (2017) studied the weighted inverse Rayleigh distribution. Jan, Fatima, and Ahmad (2017) studied the weighted Ailamujia distribution and found its applications to real data sets.…”

Section: Introductionmentioning

confidence: 99%

Weighted Version of Generalized Inverse Weibull Distribution

Mudiasir

Ahmad

2019

J. Mod. Appl. Stat. Methods

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Weighted distributions are used in many fields, such as medicine, ecology, and reliability. A weighted version of the generalized inverse Weibull distribution, known as weighted generalized inverse Weibull distribution (WGIWD), is proposed. Basic properties including mode, moments, moment generating function, skewness, kurtosis, and Shannon’s entropy are studied. The usefulness of the new model was demonstrated by applying it to a real-life data set. The WGIWD fits better than its submodels, such as length biased generalized inverse Weibull (LGIW), generalized inverse Weibull (GIW), inverse Weibull (IW) and inverse exponential (IE) distributions.

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On Weighted Ailamujia Distribution and Its Applications to Lifetime Data

Cited by 12 publications

References 1 publication

Untitled

Untitled

Transmuted Ailamujia Distribution with Applications to Lifetime Observations

Weighted Version of Generalized Inverse Weibull Distribution

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