TY - JOUR

T1 - The gamma log-logistic Weibull distribution: model, properties and application

AU - Foya, Susan

AU - Oluyede, Broderick O.

AU - Fagbamigbe, Adeniyi F.

AU - Makubate, Boikanyo

PY - 2017/4/1

Y1 - 2017/4/1

N2 - In this paper, a new generalized distribution called the gamma log-logistic Weibull (GLLoGW) distribution is proposed and studied. The GLLoGW distribution include the gamma log-logistic, gamma log-logistic Rayleigh, gamma log logistic exponential, log-logistic Weibull, log-logistic Rayleigh, log-logistic exponential, log-logistic as well as other special cases as sub models. Some mathematical properties of the new distribution including moments, conditional moments, mean and median deviations, Bonferroni and Lorenz curves, distribution of the order statistics and Renyi entropy are derived. Maximum likelihood estimation technique is used to estimate the model parameters. A Monte Carlo simulation study to examine the bias and mean square error of the maximum likelihood estimators is presented and an application to real dataset to illustrate the usefulness of the model is given.

AB - In this paper, a new generalized distribution called the gamma log-logistic Weibull (GLLoGW) distribution is proposed and studied. The GLLoGW distribution include the gamma log-logistic, gamma log-logistic Rayleigh, gamma log logistic exponential, log-logistic Weibull, log-logistic Rayleigh, log-logistic exponential, log-logistic as well as other special cases as sub models. Some mathematical properties of the new distribution including moments, conditional moments, mean and median deviations, Bonferroni and Lorenz curves, distribution of the order statistics and Renyi entropy are derived. Maximum likelihood estimation technique is used to estimate the model parameters. A Monte Carlo simulation study to examine the bias and mean square error of the maximum likelihood estimators is presented and an application to real dataset to illustrate the usefulness of the model is given.

U2 - 10.1285/i20705948v10n1p206

DO - 10.1285/i20705948v10n1p206

M3 - Article

VL - 10

SP - 206

EP - 241

JO - Electronic Journal of Applied Statistical Analysis

JF - Electronic Journal of Applied Statistical Analysis

SN - 2070-5948

IS - 1

ER -