Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288420 
Year of Publication: 
2020
Citation: 
[Journal:] Statistical Methods & Applications [ISSN:] 1613-981X [Volume:] 30 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 1135-1155
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We consider Bayesian sample size determination using a criterion that utilizes the first two moments of the posterior variance. We study the resulting sample size in dependence on the chosen prior and explore the success rate for bounding the posterior variance below a prescribed limit under the true sampling distribution. Compared with sample size determination based on the average of the posterior variance the proposed criterion leads to an increase in sample size and significantly improved success rates. Generic asymptotic properties are proven, such as an asymptotic expression for the sample size and a sort of phase transition. Our study is illustrated using two real world datasets with Poisson and normally distributed data. Based on our results some recommendations are given.
Subjects: 
Statistics, general
Statistical Theory and Methods
Statistics for Business, Management, Economics, Finance, Insurance
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Statistics for Life Sciences, Medicine, Health Sciences
Statistics for Social Sciences, Humanities, Law
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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