scholarly journals Estimation of Parameters of Topp-Leone Inverse Lomax Distribution in Presence of Right Censored Samples

Author(s):  
Amal SOLİMAN ◽  
Doaa ISMAİL
Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ramadan A. ZeinEldin ◽  
Muhammad Ahsan ul Haq ◽  
Sharqa Hashmi ◽  
Mahmoud Elsehety ◽  
M. Elgarhy

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.


2019 ◽  
Vol 8 (1) ◽  
pp. 17-22
Author(s):  
Obubu Maxwell ◽  
Agu Friday ◽  
Nwokike Chukwudike ◽  
Francis Runyi ◽  
Offorha Bright

2021 ◽  
Vol 09 (04) ◽  
pp. 351-369
Author(s):  
Mohamed M. Buzaridah ◽  
Dina A. Ramadan ◽  
Beih S. El-Desouky

Author(s):  
Obubu Maxwell ◽  
Angela Unna Chukwu ◽  
Oluwafemi Samuel Oyamakin ◽  
Mundher A. Khaleel

A new compound distribution called the Marshall-Olkin Inverse Lomax distribution (MO-ILD) was proposed, extending the inverse Lomax distribution by adding a new parameter to the existing distribution, leading to greater flexibility in modeling various data types. Its basic statistical properties were derived and model parameters estimated using the method of maximum likelihood. The Proposed distribution was applied to Cancer Stem Cell data and compared to the Marshall Olkin Flexible Weibull Extension Distribution (MO-FWED), and the Marshall-Olkin exponential Weibull distribution (MO-EWD). The Marshall-Olkin Inverse Lomax distribution provided a better fit than the Marshall Olkin Flexible Weibull Extension Distribution, and the Marshall-Olkin exponential Weibull distribution based on log-likelihood AIC, CAIC, BIC and HQIC values.


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