КФУ. Карточка публикации. On asymptotic expansion of posterior distribution

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ON ASYMPTOTIC EXPANSION OF POSTERIOR DISTRIBUTION

Форма представления

Статьи в российских журналах и сборниках

Год публикации

2016

Язык

английский

Авторы, сотрудники КФУ

Заикин Артем Александрович, автор

Библиографическое описание на языке оригинала

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

Аннотация

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.

Ключевые слова

Bayesian analysis, posterior distribution, asymptotic expansion

Название журнала

Lobachevskii Journal of Mathematics

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https://repository.kpfu.ru/?p_id=135542

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Заикин Артем Александрович	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/?p_id=135542	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	Статьи в российских журналах и сборниках	ru_RU