An Extended Burr XII Distribution: Properties, Inference and Applications
Pakistan Journal of Statistics and Operation Research • 2017
Publication Information
Authors
Gauss M. Cordeiro;M. E. Mead;Ahmed Z. Afify;Adriano K. Suzuki;Amarat A. K. Abd El-Gaied
Keywords
Exponentiated Burr XII; Hazard Function; Marshall-Olkin Family; Maximum Likelihood; Order Statistic;Rényi entropy
Maximum Likelihood, Order Statistic; Rényi entropy
Journal
Pakistan Journal of Statistics and Operation Research
Publisher
University of the Punjab
Volume
13
Issue
4
Pages
809-828
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
We propose and study a new continuous model named the Marshall-Olkin exponentiated Burr XII
(MOEBXII) distribution. It contains se veral special cases, namely the Marshall-Olkin exponentiated loglogistic, Marshall-Olkin exponentiated Lomax, Marshall-Olkin Burr XII, Marshall-Olkin log-logistic,
Marshall-Olkin Lomax distributions, among others, and most importantly includes all four of the most
common types of hazard function: monotonically increasing or decreasing, bathtub and arc -shaped hazard
functions. Some of its structural properties are obtained such as the ordinary and incomplete moments,
quantile and generating functions, order statistics and probability weighted moments. The maximum
likelihood and least square methods are used to estimate the model parame ters. A simulation study is
performed to evaluate the precision of the estimates from both methods. The usefulness of the new model
is illustrated by means of two real data sets.
(MOEBXII) distribution. It contains se veral special cases, namely the Marshall-Olkin exponentiated loglogistic, Marshall-Olkin exponentiated Lomax, Marshall-Olkin Burr XII, Marshall-Olkin log-logistic,
Marshall-Olkin Lomax distributions, among others, and most importantly includes all four of the most
common types of hazard function: monotonically increasing or decreasing, bathtub and arc -shaped hazard
functions. Some of its structural properties are obtained such as the ordinary and incomplete moments,
quantile and generating functions, order statistics and probability weighted moments. The maximum
likelihood and least square methods are used to estimate the model parame ters. A simulation study is
performed to evaluate the precision of the estimates from both methods. The usefulness of the new model
is illustrated by means of two real data sets.
Staff Members - Benha University