Advances and Applications in Statistics
Volume 9, Issue 1, Pages 13 - 35
(June 2008)
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BAYESIAN INFERENCE FOR BINOMIAL POPULATIONS. BAYESIAN ESTIMATION FOR CHILD DEPRESSION PREVALENCE
Joan Guತia-Olmos (Spain), Emilia I. de la Fuente-Solana (Spain) and Luis Manuel Lozano-Fernᮤez (Spain)
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Abstract: The estimation procedures based on Bayes? theorem are still an unusual option in many of the environments of classic parametric inference. One of those favourable fields and with a strong statistical tradition is sanitary epidemiology. The aim of this paper is to show an effective scheme for the use of Bayesian estimation of unknown parameters. For this reason, we have opted to focus on the estimation of parameters under the assumption of a binomial model, so that it can be followed by all those situations that meet the aforementioned probabilistic model. This approximation was studied in comparison with the classic parametric approximation, both in its point version and by means of interval estimation. On a first study, by simulating samples of several sizes, we obtained empirical evidence regarding the advantage of the Bayesian procedure in the cases of smaller size samples. Likewise, both procedures were applied to some data collected in order to obtain the estimation of point prevalence in child depression. The results in this second study showed the same tendency as the first study simulated in favour of Bayesian estimation. |
Keywords and phrases: Bayesian inference, binomial distribution, prevalence, depression. |
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