Hamsyiah, Nurmaita and Nisa, Khoirin and Warsono, Warsono (2017) Parameter Estimation of Bernoulli Distribution using Maximum Likelihood and Bayesian Methods. Prosiding Seminar Nasional METODE KUANTITATIF 2017. ISSN 978-602-98559-3-7


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The term parameter estimation refers to the process of using sample data to estimate the parameters of the selected distribution.There are several methodsthat can be used to estimate distributionparameter(s).In this paper,the maximum likelihood andBayesian methodsare usedfor estimating parameter ofBernoulli distribution, i.e. , which isdefined asthe probability of success event for two possible outcomes.The maximum likelihood and Bayesian estimators of Bernoulli parameter are derived, for the Bayesian estimator the Beta prior is used. The analytical calculation shows that maximum likelihood estimator is unbiased while Bayesian estimator is asymptotically unbiased. However, empirical analysis by Monte Carlo simulation shows that the mean square errors (MSE) of the Bayesian estimatorare smaller than maximum likelihood estimator for large sample sizes.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Depositing User: DR. KHOIRIN NISA
Date Deposited: 27 Mar 2018 04:42
Last Modified: 27 Mar 2018 04:42
URI: http://repository.lppm.unila.ac.id/id/eprint/6648

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