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All Journal African Multidisciplinary Journal of Sciences and Artificial Intelligence Kwaghe International Journal of Sciences and Technology Kwaghe International Journal of Arts, Humanities and Religious Studies
Jamilu Yunusa Falgore
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Social Sciences

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The Weibull-Exponentiated Exponential Cure Fraction Model for Right Censored Survival Data with Applications to Cancer Data Aliyu Yakubu; Naziru Isah Muhammad; Jamilu Yunusa Falgore; Adam Rabiu
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 1 No 2 (2024): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v1i2.3855

Abstract

The cure fraction model also known as the long-term survival model is used in fitting data from a population with two different types of individuals: individuals who experienced the event of interest (susceptible) and individuals who will never experience the event of interest (non-susceptible). The present paper introduced a cure fraction model considering the Weibull exponentiated exponential distribution that will be used in modeling such type of information. The parameters of the model were estimated via the maximum likelihood procedure (MLE) under the assumption of right censoring. Furthermore, the statistical properties of the model were studied comprehensively. Simulation studies and medical data sets were used to demonstrate the applicability of the proposed methodology. Bias and standard error were used as discrimination criteria in the simulation study while Akaike Information criteria (AIC), Bayesian Information Criteria (BIC), and Consistent Akaike Information criteria (CAIC) were used as discrimination criteria in real-life applications. Results from the applications showed that the Weibull exponentiated exponential non-mixture cure fraction model is a strong competitor.
A New Inverse Lomax Weibull-G Family of Distributions with Applications Jamilu Yunusa Falgore; Yahaya Abubakar; Sani Ibrahim Doguwa; Aminu Suleiman Mohammed; Abdussamad Tanko Imam
Kwaghe International Journal of Sciences and Technology Vol 1 No 1 (2024): Kwaghe International Journal of Sciences and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijst.v1i1.3721

Abstract

The field of statistics is constantly evolving, and new approaches are being developed to model real-world datasets. Despite this, there are still many significant concerns surrounding real data that remain unresolved by existing approaches. One of the drawbacks of the Inverse Lomax distribution is that it belongs to the inverted family of distributions, which limits its application and makes it unsuitable for some situations. Based on these, a new family of distributions called Inverse Lomax Weibull G (ILWG) based on the Inverse Lomax-G and Weibull-G was proposed in this study. Some statistical properties of the family such as the quantile function, moments, and characteristic function were presented. Exponential distribution was used as a member of this family to demonstrate the applicability of the new family. Some statistical properties of the Inverse Lomax Weibull exponential distribution (ILWED) such as quantile function, moments, and characteristic function were demonstrated. ILWED's shapes can be right skewed and symmetric, as the case maybe. Sample quantiles were presented. A simulation study was also presented to explore the desirable properties of the ILWED. Lastly, an application to three (3) different datasets was demonstrated based on the ILWED.
The Weibull-Exponentiated Exponential Cure Fraction Model for Right Censored Survival Data with Applications to Cancer Data Aliyu Yakubu; Naziru Isah Muhammad; Jamilu Yunusa Falgore; Adam Rabiu
Kwaghe International Journal of Arts, Humanities and Religious Studies Vol 1 No 1 (2024): Kwaghe International Journal of Arts, Humanities and Religious Studies
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijahrs.v1i1.3821

Abstract

The cure fraction model also known as the long-term survival model is used in fitting data from a population with two different types of individuals: individuals who experienced the event of interest (susceptible) and individuals who will never experience the event of interest (non-susceptible). The present paper introduced a cure fraction model considering the Weibull exponentiated exponential distribution that will be used in modeling such type of information. The parameters of the model were estimated via the maximum likelihood procedure (MLE) under the assumption of right censoring. Furthermore, the statistical properties of the model were studied comprehensively. Simulation studies and medical data sets were used to demonstrate the applicability of the proposed methodology. Bias and standard error were used as discrimination criteria in the simulation study while Akaike Information criteria (AIC), Bayesian Information Criteria (BIC), and Consistent Akaike Information criteria (CAIC) were used as discrimination criteria in real-life applications. Results from the applications showed that the Weibull exponentiated exponential non-mixture cure fraction model is a strong competitor.
Co-Authors Abdussamad Tanko Imam Adam Rabiu Aliyu Yakubu Aminu Suleiman Mohammed Naziru Isah Muhammad Sani Ibrahim Doguwa Yahaya Abubakar