dc.contributor.author |
SAWADOGO, Ibrahim |
|
dc.date.accessioned |
2018-06-26T07:01:58Z |
|
dc.date.available |
2018-06-26T07:01:58Z |
|
dc.date.issued |
2018-06-26 |
|
dc.identifier.citation |
SAWADOGO2018 |
en_US |
dc.identifier.uri |
http://hdl.handle.net/123456789/4668 |
|
dc.description |
degree of Master of Science in Mathematics |
en_US |
dc.description.abstract |
The Exponentiated Generalized Weibull distribution is a probability distribution which
generalizes the Weibull distribution, introducing two more shapes parameters to best
adjust the non-monotonic failure rate. The distribution was derived by Oguntunde et al.
in 2015 based on Codeiro et al.’s paper on the exponentiated generalized class of distribution.
The parameters of the new probability distribution function are estimated by the
maximum likelihood method under progressive type II censored data via Expectation
Maximization (EM) algorithm. The performance of estimators are investigated using
the Root Mean Square Error RMSE based on simulation for various degrees of censoring
and sample sizes. Application to real data is included. It is observed that RMSE
decreases with increasing sample size, and also with decreasing censored sample size. |
en_US |
dc.description.sponsorship |
Name: Prof Leo ODONGO
Department of Statistics and Actuarial Science, Kenyatta University (KU), Nairobi,
Kenya.
This research thesis has been submitted for examination with my approval as University
Supervisor.
Signature . . Date
Name: Dr Ibrahim LY
Department of Mathematics, University of Ouagadougou, Ouagadougou, Burkina Faso.
Department of Mathematics, University of Potsdam, Potsdam, Germany |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
JKUAT |
en_US |
dc.subject |
Maximum Likelihood |
en_US |
dc.subject |
Estimation |
en_US |
dc.subject |
parameters |
en_US |
dc.subject |
Exponentiated Generalized |
en_US |
dc.subject |
Weibull Distribution |
en_US |
dc.title |
Maximum Likelihood Estimation of the parameters of Exponentiated Generalized Weibull Distribution based on progressive Type II censored data |
en_US |
dc.type |
Thesis |
en_US |