Properties of the Transmuted Burr XII Distribution, Regression and its Applications

Afify, Ahmed Z.; Cordeiro, Gauss M.; Bourguignon, Marcelo; Ortega, María Montserrat Martínez

doi:10.6339/jds.201807_16(3).0003

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…The first set of data represents the gauge lengths of 20 mm and it consists of 74 observations. This data is reported in [23], and it is studied by [24,25] and [26]. The second set of data consists of n = 128 observations of remission times (months) of bladder cancer patients studied by [27].…”

Section: Applicationsmentioning

confidence: 99%

Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine

Afify¹,

Kumar²,

Elbatal³

2020

JSTA

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This paper proposes a new flexible four-parameter model called Marshall-Olkin power generalized Weibull (MOPGW) distribution which provides symmetrical, reversed-J shaped, left-skewed and right-skewed densities, and bathtub, unimodal, increasing, constant, decreasing, J shaped, and reversed-J shaped hazard rates. Some of the MOPGW structural properties are discussed. The maximum likelihood is utilized to estimate the MOPGW unknown parameters. Simulation results are provided to assess the performance of the maximum likelihood method. Finally, we illustrate the importance of the MOPGW model, compared with some rival models, via two real data applications from the engineering and medicine fields.

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Section: Applicationsmentioning

confidence: 99%

Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine

Afify¹,

Kumar²,

Elbatal³

2020

JSTA

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“…The T-G family has received widespread recognition in the literature and more than 70 generalized models have been proposed based on the T-G class. For example, the transmuted log-logistic [6], transmuted Marshall-Olkin Fréchet [7], and transmuted Burr XII [8], amongst others. Tahir and Cordeiro [9] listed more than 50 generalized models that were extended using the T-G family.…”

Section: Introductionmentioning

confidence: 99%

The Flexible Burr X-G Family: Properties, Inference, and Applications in Engineering Science

et al. 2021

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In this paper, we introduce a new flexible generator of continuous distributions called the transmuted Burr X-G (TBX-G) family to extend and increase the flexibility of the Burr X generator. The general statistical properties of the TBX-G family are calculated. One special sub-model, TBX-exponential distribution, is studied in detail. We discuss eight estimation approaches to estimating the TBX-exponential parameters, and numerical simulations are conducted to compare the suggested approaches based on partial and overall ranks. Based on our study, the Anderson–Darling estimators are recommended to estimate the TBX-exponential parameters. Using two skewed real data sets from the engineering sciences, we illustrate the importance and flexibility of the TBX-exponential model compared with other existing competing distributions.

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“…There are papers that perform a good work obtaining the solutions. For instance: the three parameter log-xgamma Weibull regression model [1], the four parameter Topp Leone generated Burr XII [2], log-odd log-logistic Marshal Olkin generalized half-normal [3] and log-beta Burr XII [4] regression models, and the five parameter log-Hjorth Weibull regression model [5]. We note that many of these complex distributions suffer from the interpretation of the parameters and their estimations needed whenever predictions are demanded.…”

Section: Introductionmentioning

confidence: 99%

Comparison between Highly Complex Location Models and GAMLSS

Ramires

Nakamura

Righetto³

et al. 2021

Entropy

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This paper presents a discussion regarding regression models, especially those belonging to the location class. Our main motivation is that, with simple distributions having simple interpretations, in some cases, one gets better results than the ones obtained with overly complex distributions. For instance, with the reverse Gumbel (RG) distribution, it is possible to explain response variables by making use of the generalized additive models for location, scale, and shape (GAMLSS) framework, which allows the fitting of several parameters (characteristics) of the probabilistic distributions, like mean, mode, variance, and others. Three real data applications are used to compare several location models against the RG under the GAMLSS framework. The intention is to show that the use of a simple distribution (e.g., RG) based on a more sophisticated regression structure may be preferable than using a more complex location model.

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Properties of the Transmuted Burr XII Distribution, Regression and its Applications

Cited by 6 publications

References 26 publications

Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine

Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine

The Flexible Burr X-G Family: Properties, Inference, and Applications in Engineering Science

Comparison between Highly Complex Location Models and GAMLSS

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