Bayesian Estimation of Measles Vaccination Coverage Under Ranked Set Sampling

Das Radhakanta (), Verma Vivek () and Nath Dilip C. ()
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Das Radhakanta: Department of Statistics, Presidency University, 86/1 College Street Kolkata, 700073, West Bengal, India
Verma Vivek: Department of Statistics, Gauhati University, Guwahati, 781014, Assam, India
Nath Dilip C.: Department of Statistics, Gauhati University, Guwahati, 781014, Assam, India

Statistics in Transition New Series, 2017, vol. 18, issue 4, 589-608

Abstract: The present article is concerned with the problem of estimating an unknown population proportion p, say, of a certain population characteristic in a dichotomous population using the data collected through ranked set sampling (RSS) strategy. Here, it is assumed that the proportion p is not fixed but a random quantity. A Bayes estimator of p is proposed under squared error loss function assuming that the prior density of p belongs to the family of Beta distributions. The performance of the proposed RSS-based Bayes estimator is compared with that of the corresponding classical version estimator based on maximum likelihood principle. The proposed procedure is used to estimate measles vaccination coverage probability among the children of age group 12-23 months in India using the real-life epidemiological data from National Family Health Survey-III.

Keywords: Bayes estimator; maximum likelihood principle; square error loss; risk function and immunization coverage (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:18:y:2017:i:4:p:589-608:n:9

DOI: 10.21307/stattrans-2017-002

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