For the first time, a five-parameter distribution, called the kumaraswamy quadratic hazard rate distribution is defined and studied. The new distribution contains as special models some well-known distributions discussed in lifetime literature, such as the Linear failure rate, Exponential and Rayleigh distributions, among several others. We obtain the moments, moment generating and quantile functions. We discuss the method of maximum likelihood to estimate the model parameters and determine the observed information matrix. A real data sets illustrate the importance and flexibility of the proposed models.
%0 Journal Article
%1 elbatal2013new
%A Elbatal, Ibrahim
%A Butt, Nadeem Shafique N.S.
%D 2013
%I Punjab University Press
%J Pakistan Journal of Statistics and Operation Research
%K Order estimation likelihood statistics,[Maximum
%N 4
%P 343--361
%R 10.18187/pjsor.v9i4.678
%T A New Generalization of Quadratic Hazard Rate Distribution
%U http://www.pjsor.com/index.php/pjsor/article/view/678
%V 9
%X For the first time, a five-parameter distribution, called the kumaraswamy quadratic hazard rate distribution is defined and studied. The new distribution contains as special models some well-known distributions discussed in lifetime literature, such as the Linear failure rate, Exponential and Rayleigh distributions, among several others. We obtain the moments, moment generating and quantile functions. We discuss the method of maximum likelihood to estimate the model parameters and determine the observed information matrix. A real data sets illustrate the importance and flexibility of the proposed models.
@article{elbatal2013new,
abstract = {For the first time, a five-parameter distribution, called the kumaraswamy quadratic hazard rate distribution is defined and studied. The new distribution contains as special models some well-known distributions discussed in lifetime literature, such as the Linear failure rate, Exponential and Rayleigh distributions, among several others. We obtain the moments, moment generating and quantile functions. We discuss the method of maximum likelihood to estimate the model parameters and determine the observed information matrix. A real data sets illustrate the importance and flexibility of the proposed models.},
added-at = {2017-11-05T18:54:27.000+0100},
author = {Elbatal, Ibrahim and Butt, Nadeem Shafique N.S.},
biburl = {https://www.bibsonomy.org/bibtex/29665fc94f5965683a300b3fa7ad99c70/nadeemshafique},
doi = {10.18187/pjsor.v9i4.678},
file = {:C$\backslash$:/Users/owner/Dropbox/PJSOR/Issues/PJSOR (All Volumes)/22 Volume IX, No.4/Paper 1 (Ibrahim Elbatal).pdf:pdf},
interhash = {db776e2fb94d53204239cc5e8905af26},
intrahash = {9665fc94f5965683a300b3fa7ad99c70},
issn = {2220-5810},
journal = {Pakistan Journal of Statistics and Operation Research},
keywords = {Order estimation likelihood statistics,[Maximum},
month = feb,
number = 4,
pages = {343--361},
publisher = {Punjab University Press},
timestamp = {2017-11-05T18:55:24.000+0100},
title = {{A New Generalization of Quadratic Hazard Rate Distribution}},
url = {http://www.pjsor.com/index.php/pjsor/article/view/678},
volume = 9,
year = 2013
}