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

2021 ◽  
Author(s):  
Amal SOLİMAN ◽  
Doaa ISMAİL
Keyword(s):  
Estimation Of Parameters ◽  
Lomax Distribution ◽  
Censored Samples ◽  
Inverse Lomax Distribution

scholarly journals Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ramadan A. ZeinEldin ◽  
Muhammad Ahsan ul Haq ◽  
Sharqa Hashmi ◽  
Mahmoud Elsehety ◽  
M. Elgarhy
Keyword(s):  
Goodness Of Fit ◽  
Continuous Distribution ◽  
Information Criterion ◽  
Value Theory ◽  
Estimation Methods ◽  
Parameter Distribution ◽  
Monte Carlo Simulation Study ◽  
Lifetime Distributions ◽  
Lomax Distribution ◽  
Inverse Lomax Distribution

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.

scholarly journals A theoretical analysis of the odd generalized exponentiated inverse Lomax distribution

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

scholarly journals Flexible Reduced Logarithmic-Inverse Lomax Distribution with Application for Bladder Cancer

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

scholarly journals Reliability Estimation of Inverse Lomax Distribution Using Extreme Ranked Set Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Amer Ibrahim Al-Omari ◽  
Amal S. Hassan ◽  
Naif Alotaibi ◽  
Mansour Shrahili ◽  
Heba F. Nagy
Keyword(s):  
Ranked Set Sampling ◽  
Reliability Estimation ◽  
Simple Random Sample ◽  
Stress Distributions ◽  
Reliability Estimate ◽  
Lomax Distribution ◽  
Set Size ◽  
Reliability Estimates ◽  
Inverse Lomax Distribution ◽  
Extreme Ranked Set Sampling

In survival analysis, the two-parameter inverse Lomax distribution is an important lifetime distribution. In this study, the estimation of R = P   Y < X is investigated when the stress and strength random variables are independent inverse Lomax distribution. Using the maximum likelihood approach, we obtain the R estimator via simple random sample (SRS), ranked set sampling (RSS), and extreme ranked set sampling (ERSS) methods. Four different estimators are developed under the ERSS framework. Two estimators are obtained when both strength and stress populations have the same set size. The two other estimators are obtained when both strength and stress distributions have dissimilar set sizes. Through a simulation experiment, the suggested estimates are compared to the corresponding under SRS. Also, the reliability estimates via ERSS method are compared to those under RSS scheme. It is found that the reliability estimate based on RSS and ERSS schemes is more efficient than the equivalent using SRS based on the same number of measured units. The reliability estimates based on RSS scheme are more appropriate than the others in most situations. For small even set size, the reliability estimate via ERSS scheme is more efficient than those under RSS and SRS. However, in a few cases, reliability estimates via ERSS method are more accurate than using RSS and SRS schemes.

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

2019 ◽  
pp. 1-12 ◽  
Author(s):  
Obubu Maxwell ◽  
Angela Unna Chukwu ◽  
Oluwafemi Samuel Oyamakin ◽  
Mundher A. Khaleel
Keyword(s):  
Stem Cell ◽  
Cancer Stem Cell ◽  
Weibull Distribution ◽  
Model Parameters ◽  
Data Types ◽  
Lomax Distribution ◽  
Method Of Maximum Likelihood ◽  
Compound Distribution ◽  
Inverse Lomax Distribution ◽  
Cell Data

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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