KFU. Card publication. On asymptotic expansion of posterior distribution

KAZAN
FEDERAL UNIVERSITY

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ON ASYMPTOTIC EXPANSION OF POSTERIOR DISTRIBUTION

Form of presentation

Articles in Russian journals and collections

Year of publication

2016

Язык

английский

Authors, employees of KFU

Zaikin Artem Aleksandrovich, author

Bibliographic description in the original language

Zaikin A.A. On asymptotic expansion of posterior distribution // Lobachevskii Journal of Mathematics. - 2016 - Volume 37, Issue 4. - pp. 515–525.

Annotation

The paper suggests a new asymptotic expansion of posterior distribution, which improves the known normal asymptotic. The main difference from the previous works on this subject is that the suggested expansion is calculated for the deviation from the true parameter value and not from the value of the maximum likelihood estimator, as it has been done before. This setting is more appropriate for Bayesian and d-posterior approaches to a statistical inference problem. The new expansion can be derived under weaker assumptions than the previously known. Moreover, an asymptotic expansion for the moments of posterior distribution is also presented. The accuracy of the expansion is tested on binomial model with beta prior and results are compared to the Johnson?s expansion.

Keywords

Bayesian analysis, posterior distribution, asymptotic expansion

The name of the journal

Lobachevskii Journal of Mathematics

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=135542&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Zaikin Artem Aleksandrovich	ru_RU
dc.date.accessioned	2016-01-01T00:00:00Z	ru_RU
dc.date.available	2016-01-01T00:00:00Z	ru_RU
dc.date.issued	2016	ru_RU
dc.identifier.citation	Zaikin A.A. On asymptotic expansion of posterior distribution // Lobachevskii Journal of Mathematics. - 2016 - Volume 37, Issue 4. - pp. 515–525.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=135542&p_lang=2	ru_RU
dc.description.abstract	Lobachevskii Journal of Mathematics	ru_RU
dc.description.abstract	The paper suggests a new asymptotic expansion of posterior distribution, which improves the known normal asymptotic. The main difference from the previous works on this subject is that the suggested expansion is calculated for the deviation from the true parameter value and not from the value of the maximum likelihood estimator, as it has been done before. This setting is more appropriate for Bayesian and d-posterior approaches to a statistical inference problem. The new expansion can be derived under weaker assumptions than the previously known. Moreover, an asymptotic expansion for the moments of posterior distribution is also presented. The accuracy of the expansion is tested on binomial model with beta prior and results are compared to the Johnson?s expansion.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Bayesian analysis	ru_RU
dc.subject	posterior distribution	ru_RU
dc.subject	asymptotic expansion	ru_RU
dc.title	On asymptotic expansion of posterior distribution	ru_RU
dc.type	Articles in Russian journals and collections	ru_RU