The Marshall-olkin Inverse Lomax Distribution (MO-ILD) with Application on Cancer Stem Cell

Maxwell, Obubu; Chukwu, A.; Oyamakin, S. O.; Khaleel, Mundher A.

doi:10.9734/jamcs/2019/v33i430186

Cited by 22 publications

(15 citation statements)

References 12 publications

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“…Weibull IL distribution has been proposed by Hassan and Mohamed [12]. Maxwell et al [13] proposed and studied Marshall-Olkin IL distribution and compared this new model with other competitive models using real data applications. ZeinEldin et al [14] proposed and alpha-power transformed IL distribution.…”

Section: Introductionmentioning

confidence: 99%

Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications

et al. 2020

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In this article, we propose and study a new three-parameter distribution, called the odd Fréchet inverse Lomax (OFIL) distribution, derived by combining the odd Fréchet-G family and the inverse Lomax distribution. Since Fréchet is a continuous distribution with wide applicability in extreme value theory, the new model contains these properties as well as the characteristics of the inverse Lomax distribution which make it more flexible and provide a good alternative for some well-known lifetime distributions. We initially present a linear representation of its functions and discussion on density and hazard rate function. Then, we study its various mathematical properties. Different estimation methods are used to estimate parameters of OFIL. The Monte Carlo simulation study is carried out to compare the efficiencies of different methods of estimation. The maximum likelihood estimation (MLE) method is used to estimate the OFIL parameters by considering three practical data applications. We show that the related model is the best in comparisons based on Akaike information criterion (AIC), Bayesian information criterion (BIC), and other goodness-of-fit measures.

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Section: Introductionmentioning

confidence: 99%

Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications

et al. 2020

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“…In this section, we illustrate the applicability of the WIL distribution to the monthly remission of the 128 bladder cancer patients as reported by Maxwell et al 23 We estimated the parameters of each model by the method of Maximum Likelihood Estimation (MLE) using Simulated ANNealing (SANN) method. The goodness of fit statistics used in comparing the performances are Akaike Information Criterion AIC and Bayesian Information Criterion.…”

Section: Applicationmentioning

confidence: 99%

“…Some details about the Inverse Lomax distribution including its applications are available in Reyad and Othman, Falgore et al, Maxwell et al, and Hassan and Mohamed. [17][18][19][20][21][22][23] The main aim of this paper is to provide an extension of the Inverse-Lomax distribution using the Weibull-G generator by Bourguignon et al 6 Therefore, we propose the Weibull-Inverse lomax (WIL) distribution by adding two extra shape parameters to the Inverse Lomax distribution.…”

Section: Introductionmentioning

confidence: 99%

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2019

BBIJ

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In this paper, we introduced a four-parameter probability model called Weibull-Inverse Lomax distribution with decreasing, increasing and bathtub hazard rate function. The WIL distribution density function is J-shaped, positively skewed, and J-shaped in reverse. Some of the mentioned distribution's statistical characteristics are provided including moments, order statistics, entropy, mean, variance, moment generating function, and quantile function. The method of maximum likelihood estimation was used to estimate the parameters of the model. The distribution's importance is proved by its implementation to the bladder cancer data set. Goodness-of-fit of this distribution by various techniques demonstrates that the WIL distribution is empirically better for lifetime application.

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“…Despite an interesting compromise between simplicity and accuracy, the IL model suffers from a certain rigidity in the peak (punctual and roundness) and tail properties. This motivates the developments of diverse parametric extensions, such as the inverse power Lomax distribution introduced by [10], the Weibull IL distribution studied by [11] and the Marshall-Olkin IL distribution studied by [12].…”

Section: Introductionmentioning

confidence: 96%

Half Logistic Inverse Lomax Distribution with Applications

et al. 2021

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The last years have revealed the importance of the inverse Lomax distribution in the understanding of lifetime heavy-tailed phenomena. However, the inverse Lomax modeling capabilities have certain limits that researchers aim to overcome. These limits include a certain stiffness in the modulation of the peak and tail properties of the related probability density function. In this paper, a solution is given by using the functionalities of the half logistic family. We introduce a new three-parameter extended inverse Lomax distribution called the half logistic inverse Lomax distribution. We highlight its superiority over the inverse Lomax distribution through various theoretical and practical approaches. The derived properties include the stochastic orders, quantiles, moments, incomplete moments, entropy (Rényi and q) and order statistics. Then, an emphasis is put on the corresponding parametric model. The parameters estimation is performed by six well-established methods. Numerical results are presented to compare the performance of the obtained estimates. Also, a simulation study on the estimation of the Rényi entropy is proposed. Finally, we consider three practical data sets, one containing environmental data, another dealing with engineering data and the last containing insurance data, to show how the practitioner can take advantage of the new half logistic inverse Lomax model.

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The Marshall-olkin Inverse Lomax Distribution (MO-ILD) with Application on Cancer Stem Cell

Cited by 22 publications

References 12 publications

Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications

Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications

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Half Logistic Inverse Lomax Distribution with Applications

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