Odoma Distribution and Its Application

Odom, C. C.; Ijomah, Maxwell Azubuike

doi:10.9734/ajpas/2019/v4i130103

Cited by 12 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More so, some one parameter lifetime distributions have been introduced by Shanker [7][8][9][10][11][12][13][14][15] as a 2, 3, 4, 5 and 6 component mixtures namely Akash, Shanker, Aradhana, Sujatha, Amarendra, Akshaya, Devya and Shambhu respectively. Odom [16] developed Odoma distribution using a three component mixture that keeps showing better fit when compared with some of the other one parameter distributions. These exponential-gamma mixtures share the same support:…”

Section: Original Research Articlementioning

confidence: 99%

A Model Fit Comparative Study of K-Component Mixture of One Parameter Univariate Distributions

Echebiri

Ugoh²,

Guobadia³

et al. 2022

AJPAS

View full text Add to dashboard Cite

This is a comparative study on mixture distribution; where the study seeks to ascertain whether higher number of k-component mixtures could result to development of models that show better fits. In the performance comparison, special consideration was given to univariate one parameter distributions derived using mixture models, and the results show that distributions of higher k-mixture components relatively have greater propensity to exhibit better fit than the lesser mixture component distributions (k < 3).

show abstract

Section: Original Research Articlementioning

confidence: 99%

A Model Fit Comparative Study of K-Component Mixture of One Parameter Univariate Distributions

Echebiri

Ugoh²,

Guobadia³

et al. 2022

AJPAS

View full text Add to dashboard Cite

show abstract

“…These distributions are referred to as baseline distributions when compared to the generalized forms. Some distributions in the class of Lindley are Chris-Jerry distribution proposed by Onyekwere and Obulezi [5], Ishita distribution by Shanker and Shukla [6], Rani distribution by Shanker [7], Sujatha distribution by Shanker et al [8], Pranav distribution by Shukla [9],Odoma distribution by Odom and Ijeomah [10] and Shukla distribution by Shukla et al [6], Modification of Shanker distribution by Onyekwere et al [11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study Based on Simulation of Some Methods of Classical Estimation of the Parameters of Exponentiated Lindley – Logarithmic Distribution

Musa

Onyeagu

Obulezi

2023

AJPAS

View full text Add to dashboard Cite

In this article, some non-bayesian estimation procedures of the Exponentiated Power Lindley-Logarithmic Distribution for modeling real life data are considered. A simulation study is carried out to determine the best among others using the root meas squared error values. Real life data are used to substantiate the result from the simulation study. It was discovered from simulation study and real data that the weighted least squares estimation procedure is the best method for estimating the parameters of the Exponentiated Power Lindley-Logarithmic distribution.

show abstract

“…see (1)(2)(3)(4) . One-Parameter Odoma (OPO) distribution is one of the new lifetime distributions introduced by (5) as a mixing three-components of an exponential distribution (with scale(with scale and shape 3), and gamma distribution (with scale and shape 5) Akash,… distributions. Since OPO distribution depends on one parameter and as such cannot be flexible in modeling varieties of data, we proposed Odoma (TPO) distribution was introduced by (6) .…”

Section: Introductionmentioning

confidence: 99%

New Odoma Distribution with Application to Cancer Data

Abdelall¹

2023

IJST

View full text Add to dashboard Cite

Objective: To find appropriate model more flexible than classical models for fitting the survival data in health research especially in cancer blood (Leukemia) studies by introducing some additional parameters to the basic models. Methods: Based on the Alpha Power transformation technique and using Two-Parameter Odoma distribution, the new distribution called the Alpha Power Two-Parameter Odoma distribution is introduced and studied. The maximum likelihood estimation procedure is employed to estimate the unknown parameters. A simulation study is carried out to evaluate the performance of the maximum likelihood estimators. Finding: Common statistical properties such as quantile function, rth moments, moment generating function, characteristic function, incomplete moments, entropy and order statistics are derived. The practical importance of the proposed model is illustrated by using goodness-of-fit criterias based on real data set. Novelty: The APTPO distribution is the most appropriate model for fitting survival data of cancer studies comparing with other competitive distributions.

show abstract

Odoma Distribution and Its Application

Cited by 12 publications

References 11 publications

A Model Fit Comparative Study of K-Component Mixture of One Parameter Univariate Distributions

A Model Fit Comparative Study of K-Component Mixture of One Parameter Univariate Distributions

Comparative Study Based on Simulation of Some Methods of Classical Estimation of the Parameters of Exponentiated Lindley – Logarithmic Distribution

New Odoma Distribution with Application to Cancer Data

Contact Info

Product

Resources

About