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Your search keyword '"Oguntunde, Pelumi E."' showing total 151 results
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151 results on '"Oguntunde, Pelumi E."'

17. The right truncated Xgamma-G family of distributions: Statistical properties and applications.

Author

Abbasi, Jamal N. Al, Resen, Israa Abdulameer, Abdulwahab, Abdulwahab Mohamed, Oguntunde, Pelumi E., Al-Mofleh, Hazem, and Khaleel, Mundher A.

Subjects
GENERATING functions, LORENZ curve, MAXIMUM likelihood statistics
Abstract

A new family of distributions, called the Right truncated Xgamma-G, was developed. Explicit expressions for some of its statistical properties including the Renyi entropy, moments and generating functions, stress-strength reliability, Lorenz and Benferroni curves were established. The model parameters were estimated using the maximum likelihood estimation method and real-life application was provided using three datasets. It was also demonstrated that the Right truncated Xgamma-G family of distributions fits the datasets better than the Kumaraswamy-G, Beta-G, Exponentiated Generalized-G, Weibull-G, and Gompertz-G families of distributions. [ABSTRACT FROM AUTHOR]

Published
2023
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20. Bayesian Reliability Analysis of Marshall and Olkin Model

Author

AbuJarad, Mohammed H., Khan, Athar Ali, Khaleel, Mundher A., AbuJarad, Eman S. A., AbuJarad, Ali H., and Oguntunde, Pelumi E.

Abstract

In this paper, an endeavor has been made to fit three distributions Marshall–Olkin with exponential distributions, Marshall–Olkin with exponentiated exponential distributions and Marshall–Olkin with exponentiated extension distribution keeping in mind the end goal to actualize Bayesian techniques to examine visualization of prognosis of women with breast cancer and demonstrate through utilizing Stan. Stan is an abnormal model dialect for Bayesian displaying and deduction. This model applies to a genuine survival controlled information with the goal that every one of the ideas and calculations will be around similar information. Stan code has been created and enhanced to actualize a censored system all through utilizing Stan technique. Moreover, parallel simulation tools are also implemented and additionally actualized with a broad utilization of rstan.

Published
2024
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32. The Gompertz flexible Weibull distribution and its applications

Author

Khaleel, Munddher Abdullah, Oguntunde, Pelumi E., Ahmed, M. T., Ibrahim, Noor Akma, Loh, Yue Fang, Khaleel, Munddher Abdullah, Oguntunde, Pelumi E., Ahmed, M. T., Ibrahim, Noor Akma, and Loh, Yue Fang

Abstract

This paper introduces the Gompertz flexible Weibull distribution as an extension of the flexible Weibull distribution. Its various statistical properties are obtained and established while the method of maximum likelihood estimation is used in estimating the unknown model parameters. The application of Gompertz flexible Weibull distribution is illustrated by making use of three real life data sets, this is done to demonstrate its potentials over some other important distributions like the Gompertz Weibull, Gompertz Burr type XII, Gompertz Lomax, exponentiated flexible Weibull, exponentiated flexible Weibull extension and Kumaraswamy flexible Weibull distributions. Simulation studies were also conducted and the behavior of the Gompertz flexible Weibull parameters were investigated.

Published
2020

40. STATISTICAL PROPERTIES AND APPLICATIONS OF THE REFLECTED KUMARASWAMY WEIBULL-X {EXPONENTIAL} FAMILY OF DISTRIBUTIONS.

Author

Al Abbasi, Jamal N., Khaleel, Mundher A., and Oguntunde, Pelumi E.

Subjects
WEIBULL distribution, FISHER information, CONTINUOUS distributions, MAXIMUM likelihood statistics, DISTRIBUTION (Probability theory), RANDOM variables
Abstract

Weibull distribution is one of the most widely applied statistical distributions in various fields, especially in reliability and lifetime analysis. It has monotonically increasing, monotonically decreasing, and constant hazard rate; these however, partly limits its applications. This limitation undoubtedly inspired researchers to develop generalized Weibull distribution that can exhibit unimodal or bathtub hazard rate. In this article, a new family of continuous distributions based on the Weibull random variable called the Reflected Kumaraswamy Weibull-X {Exponential} family of distributions was introduced. General statistical properties of the new family were established and discussed, general expressions for the ordinary and incomplete moments, the generating function, quantile function, and order statistics were also provided. The maximum likelihood method of estimation was used to estimate the model parameters. In addition, the asymptotic confidence intervals for the parameters were derived from the Fisher information matrix. Real-life application to three datasets were provided and comparison was made with respect to some existing families of distributions in the literature. [ABSTRACT FROM AUTHOR]

Published
2021

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