BAYESIAN ESTIMATION OF MEASLES VACCINATION COVERAGE UNDER RANKED SET SAMPLING

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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability

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VOLUME 18 , ISSUE 4 (December 2017) > List of articles

BAYESIAN ESTIMATION OF MEASLES VACCINATION COVERAGE UNDER RANKED SET SAMPLING

Radhakanta Das / Vivek Verma * / Dilip C. Nath

Keywords : Bayes estimator, maximum likelihood principle, square error loss, risk function and immunization coverage

Citation Information : Statistics in Transition New Series. Volume 18, Issue 4, Pages 589-608, DOI: https://doi.org/10.21307/stattrans-2017-002

License : (CC BY 4.0)

Published Online: 22-January-2018

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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.

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