Maximum Likelihood Estimation of Parameters for Kumaraswamy Distribution Based on Progressive Type Ii Hybrid Censoring Scheme

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dc.contributor.advisorEdward .G. Njengaen_US
dc.contributor.authorMeymuna, Shariff Jaffer
dc.date.accessioned2023-08-09T13:12:06Z
dc.date.available2023-08-09T13:12:06Z
dc.date.issued2023
dc.descriptionA Research Project Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master of Science (Statistics) in the School of Pure and Applied Science of Kenyatta Universityen_US
dc.description.abstractThe project considers the maximum likelihood estimators for Kumaraswamy distribution based on progressive type II hybrid censoring scheme using the expectation maximization algorithm. A two parameter Kumaraswamy distribution can be applied in natural phenomena that have outcomes with an upper and a lower bound. Kumaraswamy distribution remains of keen consideration in disciplines such as economics, hydrology and survival analysis. The field of survival analysis has advanced over the years and extensive research has been undertaken. Previous studies have considered maximum likelihood estimation for Kumaraswamy distribution based on progressive type II censoring scheme using methods like Newton-Raphson and EM algorithm but none has used progressive type II hybrid censoring scheme and obtained maximum likelihood estimators of Kumaraswamy distribution via EM algorithm. EM algorithm has been utilized in manipulation of missing data as it is a more superior method when handling incomplete data. Comparison of different combinations of censoring schemes with respect to the MSEs and biases at fixed parameters of  and  are obtained through simulation. It is observed that in the three censoring schemes, for an increasing sample size, the MSEs and biases are generally decreasing. Eventually, an illustration with real life data set is provided and it illustrates how maximum likelihood estimators works in practice under different censoring schemes.en_US
dc.description.sponsorshipkenyatta universityen_US
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/26704
dc.language.isoenen_US
dc.publisherKenyatta universityen_US
dc.subjectParametersen_US
dc.subjectKumaraswamy Distribution Baseden_US
dc.subjectType Ii Hybrid Censoring Schemeen_US
dc.titleMaximum Likelihood Estimation of Parameters for Kumaraswamy Distribution Based on Progressive Type Ii Hybrid Censoring Schemeen_US
dc.typeThesisen_US
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