On the odd inverse exponential class of distributions: Properties, applications and cure fraction regression

Amadu, Yakubu; Luguterah, Albert; Nasiru, Suleman

doi:10.1080/09720510.2021.1923944

Cited by 3 publications

(6 citation statements)

References 35 publications

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“…The data is found in Abouammoh et al [31]. It can also be found in Amadu [27] and Amadu et al [28]. The ordered survival times for 40 patients are given in Table 3.…”

Section: Applications Of the Hmw-g Familymentioning

confidence: 59%

“…The applications of the special distributions (HMWL and HMWW) of the HMW-G family to real data sets in medical studies are illustrated in this section. To this end, the special distributions of the family are fitted to real data sets and their performances compared to other competing distributions including generalized inverse Weibull (GIW) distribution [25], odd generalized exponential Weibull (OGEW) distribution [26], generalized odd inverse exponential Weibull (GOIEW), and generalized odd inverse exponential Lomax (GOIEL) distributions [27,28], and exponentiated Lomax (E-Lx) distribution [29]. The total time on test (TTT) plot due to Aarset [30] is used in assessing the applicability of the special distributions to the real data sets.…”

Section: Applications Of the Hmw-g Familymentioning

confidence: 99%

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Harmonic Mixture Weibull-G Family of Distributions: Properties, Regression and Applications to Medical Data

Zamanah

Nasiru²,

Luguterah

2022

Computational and Mathematical Methods

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In recent years, the developments of new families of probability distributions have received greater attention as a result of desirable properties they exhibit in the modelling of data sets. The Harmonic Mixture Weibull-G family of distributions was developed in this study. The statistical properties were comprehensively presented and five special distributions developed from the family. The hazard functions of the special distributions were shown to exhibit various forms of monotone and nonmonotone shapes. The applications of the developed family to real data sets in medical studies revealed that the special distribution (Harmonic mixture Weibul Weibull distribution) provided a better fit to the data sets than other competitive models. A location-scale regression model was developed from the family and its application demonstrated using survival time data of hypertensive patients.

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“…The data is found in Abouammoh et al [31]. It can also be found in Amadu [27] and Amadu et al [28]. The ordered survival times for 40 patients are given in Table 3.…”

Section: Applications Of the Hmw-g Familymentioning

confidence: 59%

Section: Applications Of the Hmw-g Familymentioning

confidence: 99%

Harmonic Mixture Weibull-G Family of Distributions: Properties, Regression and Applications to Medical Data

Zamanah

Nasiru²,

Luguterah

2022

Computational and Mathematical Methods

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“…The second dataset represents the survival times of breast cancer patients obtained from 1929 to 1938. The data can be found in Ramos et al [23] and has been analyzed by several studies including Yakubu et al [24]. The observations are given as 0.3, 0.3, 4.0, 5.0, 5.6, 6.2, 6.…”

Section: Applicationsmentioning

confidence: 99%

“…The data are 0.55, 0.74, 0.77, 0.81, 0.84, 1. 24 Antle [27]. The data consists of 20 observations and are given as follows: 0.654, 0.613, 0.315, 0.449, 0.297, 0.402, 0.379, 0.423, 0.379, 0.324, 0.269, 0.740, 0.418, 0.412, 0.494, 0.416, 0.338, 0.392, 0.484, and 0.265.…”

Section: Applicationsmentioning

confidence: 99%

“…The special cases of the SKw-G family of distributions, SKwC, SKwW, SKwBXII, and SKwF distributions, are compared with Weibull Nadarajah-Haghighi (WNH) [28], generalized power Weibull (GPW) [29], exponentiated generalized Poisson inverse exponential (EGPIE) [30], Weibull inverted exponential (WIE) [31], modified Weibull (MW) [32], Kumaraswamy-Burr III (KB III) [33], complementary 12 Computational and Mathematical Methods exponential power (CEP) [34], and odd generalized exponential Weibull (OGEW) [34] distributions. Others include generalized odd inverse exponential Lomax (GOIEL) [24], Kumaraswamy inverse exponential (KIE) [35], Weibull (W) [16], odd inverse exponential Weibull (OIEW) [24], exponentiated odd inverse exponential Weibull (EOIEW) [24], new sine inverse Weibull (NSIW) [36], sine inverse Weibull (SIW) [36], inverse Weibull (IW) [37], inverted Nadarajah-Haghighi (INH) [38], the exponentiated generalized inverse Weibull (EGIW) [39], Kumaraswamy Burr III [40], and new Weibull Pareto [41] distributions. The performance of the fitted distributions is compared using log-likelihood (ℓ), Akaike information criteria (AIC), corrected Akaike information criteria (AICc), Bayesian information criteria (BIC), and the Kolmogorov-Smirnov (K-S) goodness-of-fit measure.…”

Section: Applicationsmentioning

confidence: 99%

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Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications

Nanga,

Sayibu,

Angbing

et al. 2024

Computational and Mathematical Methods

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In this study, Secant Kumaraswamy family of distributions is proposed and studied. This is motivated by the fact that no one distribution can model all types of data from different fields. Therefore, there is the need to develop distributions with desirable properties and flexible enough for modelling data exhibiting different characteristics. Some properties of the new family of distributions, including the quantile function, moments, moment generating function, and mean residual life function, are derived. Five special cases of the family of distributions are presented, and their flexibility is shown by the varying degrees of skewness and kurtosis and nonmonotonic hazard rates. The maximum likelihood estimation method is used to obtain estimators of the family of distributions. Two location-scale regression models are developed for the Secant Kumaraswamy Weibull distribution, which is a special case of the family of distributions. Six different real datasets are used to demonstrate the usefulness of the family of distributions and the regression models. The results show that the family of distributions can be used to model real datasets.

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A New Odd Beta Prime-Burr X Distribution with Applications to Petroleum Rock Sample Data and COVID-19 Mortality Rate

Suleiman,

Daud,

Singh

et al. 2023

Data

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In this article, we pioneer a new Burr X distribution using the odd beta prime generalized (OBP-G) family of distributions called the OBP-Burr X (OBPBX) distribution. The density function of this model is symmetric, left-skewed, right-skewed, and reversed-J, while the hazard function is monotonically increasing, decreasing, bathtub, and N-shaped, making it suitable for modeling skewed data and failure rates. Various statistical properties of the new model are obtained, such as moments, moment-generating function, entropies, quantile function, and limit behavior. The maximum-likelihood-estimation procedure is utilized to determine the parameters of the model. A Monte Carlo simulation study is implemented to ascertain the efficiency of maximum-likelihood estimators. The findings demonstrate the empirical application and flexibility of the OBPBX distribution, as showcased through its analysis of petroleum rock samples and COVID-19 mortality data, along with its superior performance compared to well-known extended versions of the Burr X distribution. We anticipate that the new distribution will attract a wider readership and provide a vital tool for modeling various phenomena in different domains.

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On the odd inverse exponential class of distributions: Properties, applications and cure fraction regression

Cited by 3 publications

References 35 publications

Harmonic Mixture Weibull-G Family of Distributions: Properties, Regression and Applications to Medical Data

Harmonic Mixture Weibull-G Family of Distributions: Properties, Regression and Applications to Medical Data

Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications

A New Odd Beta Prime-Burr X Distribution with Applications to Petroleum Rock Sample Data and COVID-19 Mortality Rate

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