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Inference with imperfect sampling from a Bernoulli process

Author: Gaba, Anil ; Winkler, Robert L.INSEAD Area: Technology and Operations Management In: Bayesian and likelihood methods in statistics and econometrics:essays in honor of George A. Barnard - Barnard, George A.;Geisser, Seymour - 1990 - Book Language: EnglishDescription: p. 303-317.Type of document: INSEAD ChapterNote: Please ask us for this itemAbstract: When a sample is taken from a dichotomous process, various sources of noise may cause some observvations to be classified incorrectly. In this paper, imperfect sampling from a Bernoulli process with a noise parameter that is not known is considered. A likelihood analysis reveals an identification problem, which is avoided under a Bayesian analysis with a joint prior distribution on the noise parameter to illustrate the methodology and to provide some flavour of the implications of the noise and uncertainty about the noise for inferences about a proportion.
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When a sample is taken from a dichotomous process, various sources of noise may cause some observvations to be classified incorrectly. In this paper, imperfect sampling from a Bernoulli process with a noise parameter that is not known is considered. A likelihood analysis reveals an identification problem, which is avoided under a Bayesian analysis with a joint prior distribution on the noise parameter to illustrate the methodology and to provide some flavour of the implications of the noise and uncertainty about the noise for inferences about a proportion.

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