A Generalized Class of Exponentiated Modified Weibull Distribution With Applications

Pu, Shusen; Oluyede, Broderick O.; Qiu, Yuqi; Linder, Daniel F.

doi:10.6339/jds.201610_14(4).0002

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…Furthermore, Figure 2a,b presents the hazard rate function (HRF) shapes, including increasing, U, and bathtub shapes. These flexible HRF shapes are suitable for both the monotonic and non-monotonic hazard rate behaviors, which are most likely to appear in real-time situations (see Pu et al [27] and Oluyede et al [28]). Such kinds of shapes are often observed in non-stationary lifetime phenomena.…”

Section: Shapementioning

confidence: 99%

A New Extended Model with Bathtub-Shaped Failure Rate: Properties, Inference, Simulation, and Applications

et al. 2021

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Theoretical and applied researchers have been frequently interested in proposing alternative skewed and symmetric lifetime parametric models that provide greater flexibility in modeling real-life data in several applied sciences. To fill this gap, we introduce a three-parameter bounded lifetime model called the exponentiated new power function (E-NPF) distribution. Some of its mathematical and reliability features are discussed. Furthermore, many possible shapes over certain choices of the model parameters are presented to understand the behavior of the density and hazard rate functions. For the estimation of the model parameters, we utilize eight classical approaches of estimation and provide a simulation study to assess and explore the asymptotic behaviors of these estimators. The maximum likelihood approach is used to estimate the E-NPF parameters under the type II censored samples. The efficiency of the E-NPF distribution is evaluated by modeling three lifetime datasets, showing that the E-NPF distribution gives a better fit over its competing models such as the Kumaraswamy-PF, Weibull-PF, generalized-PF, Kumaraswamy, and beta distributions.

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Section: Shapementioning

confidence: 99%

A New Extended Model with Bathtub-Shaped Failure Rate: Properties, Inference, Simulation, and Applications

et al. 2021

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“…In recent years, numerous methodologies for constructing generalized continuous probability distributions have been proposed [12][13][14][15][16]. Contributions to this burgeoning field include the modified slash distributions [17], modified-X family of distributions [18], the new arcsine-generator distribution [19], enhanced version of the generalized Weibull distribution [20], McDonald Generalized Power Weibull distributions [21], the exponentiated XLindley distribution [22], the Pareto-Poisson distribution [23], the Kumaraswamy Generalized Inverse Lomax distribution [24], and others that have significantly advanced the statistical modeling landscape.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Real-Life Data Modeling with the Modified Burr III Odds Ratio–G Distribution

Yang,

Huang,

Chen

et al. 2024

Axioms

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In this study, we introduce the modified Burr III Odds Ratio–G distribution, a novel statistical model that integrates the odds ratio concept with the foundational Burr III distribution. The spotlight of our investigation is cast on a key subclass within this innovative framework, designated as the Burr III Scaled Inverse Odds Ratio–G (B-SIOR-G) distribution. By effectively integrating the odds ratio with the Burr III distribution, this model enhances both flexibility and predictive accuracy. We delve into a thorough exploration of this distribution family’s mathematical and statistical properties, spanning hazard rate functions, quantile functions, moments, and additional features. Through rigorous simulation, we affirm the robustness of the B-SIOR-G model. The flexibility and practicality of the B-SIOR-G model are demonstrated through its application to four datasets, highlighting its enhanced efficacy over several well-established distributions.

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“…These frameworks are highly regarded by researchers and statisticians for their capability to tailor analytical strategies to the unique challenges encountered in various datasets [1]. Generalized distributions have garnered widespread interest across numerous fields, such as epidemiology and survival analysis, due to their comprehensive applicability, as illustrated by recent models proposed in [2][3][4]. Recent additions to this domain include the transformed Log-Burr III distribution [5], the Ristić-Balakrishnan-Topp-Leone-Gompertz-G distribution [6], a novel family of modified slash distribution [7], the flexible Gumbel distribution [8], the new sine inverted exponential distribution [9], the Beta-truncated Lomax distribution [10], the bivariate Chen distribution [11], the power function-Lindley distribution [12], and the two-parameter Weibull distribution [13], among others.…”

Section: Introductionmentioning

confidence: 99%

The Lomax-Exponentiated Odds Ratio–G Distribution and Its Applications

Roy,

Knehr,

McGurk

et al. 2024

Mathematics

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This paper introduces the Lomax-exponentiated odds ratio–G (L-EOR–G) distribution, a novel framework designed to adeptly navigate the complexities of modern datasets. It blends theoretical rigor with practical application to surpass the limitations of traditional models in capturing complex data attributes such as heavy tails, shaped curves, and multimodality. Through a comprehensive examination of its theoretical foundations and empirical data analysis, this study lays down a systematic theoretical framework by detailing its statistical properties and validates the distribution’s efficacy and robustness in parameter estimation via Monte Carlo simulations. Empirical evidence from real-world datasets further demonstrates the distribution’s superior modeling capabilities, supported by compelling various goodness-of-fit tests. The convergence of theoretical precision and practical utility heralds the L-EOR–G distribution as a groundbreaking advancement in statistical modeling, significantly enhancing precision and adaptability. The new model not only addresses a critical need within statistical modeling but also opens avenues for future research, including the development of more sophisticated estimation methods and the adaptation of the model for various data types, thereby promising to refine statistical analysis and interpretation across a wide array of disciplines.

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A Generalized Class of Exponentiated Modified Weibull Distribution With Applications

Cited by 10 publications

References 27 publications

A New Extended Model with Bathtub-Shaped Failure Rate: Properties, Inference, Simulation, and Applications

A New Extended Model with Bathtub-Shaped Failure Rate: Properties, Inference, Simulation, and Applications

Enhanced Real-Life Data Modeling with the Modified Burr III Odds Ratio–G Distribution

The Lomax-Exponentiated Odds Ratio–G Distribution and Its Applications

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