On Bayesian predictive density estimation for skew-normal distributions

IF 0.9 4区 数学 Q3 STATISTICS & PROBABILITY
Metrika Pub Date : 2024-02-17 DOI:10.1007/s00184-024-00946-4
Othmane Kortbi
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引用次数: 0

Abstract

This paper is concerned with prediction for skew-normal models, and more specifically the Bayes estimation of a predictive density for \(Y \left. \right| \mu \sim {\mathcal {S}} {\mathcal {N}}_p (\mu , v_y I_p, \lambda )\) under Kullback–Leibler loss, based on \(X \left. \right| \mu \sim {\mathcal {S}} {\mathcal {N}}_p (\mu , v_x I_p, \lambda )\) with known dependence and skewness parameters. We obtain representations for Bayes predictive densities, including the minimum risk equivariant predictive density \(\hat{p}_{\pi _{o}}\) which is a Bayes predictive density with respect to the noninformative prior \(\pi _0\equiv 1\). George et al. (Ann Stat 34:78–91, 2006) used the parallel between the problem of point estimation and the problem of estimation of predictive densities to establish a connection between the difference of risks of the two problems. The development of similar connection, allows us to determine sufficient conditions of dominance over \(\hat{p}_{\pi _{o}}\) and of minimaxity. First, we show that \(\hat{p}_{\pi _{o}}\) is a minimax predictive density under KL risk for the skew-normal model. After this, for dimensions \(p\ge 3\), we obtain classes of Bayesian minimax densities that improve \(\hat{p}_{\pi _{o}}\) under KL loss, for the subclass of skew-normal distributions with small value of skewness parameter. Moreover, for dimensions \(p\ge 4\), we obtain classes of Bayesian minimax densities that improve \(\hat{p}_{\pi _{o}}\) under KL loss, for the whole class of skew-normal distributions. Examples of proper priors, including generalized student priors, generating Bayesian minimax densities that improve \(\hat{p}_{\pi _{o}}\) under KL loss, were constructed when \(p\ge 5\). This findings represent an extension of Liang and Barron (IEEE Trans Inf Theory 50(11):2708–2726, 2004), George et al. (Ann Stat 34:78–91, 2006) and Komaki (Biometrika 88(3):859–864, 2001) results to a subclass of asymmetrical distributions.

关于倾斜正态分布的贝叶斯预测密度估计
本文关注偏态模型的预测,更具体地说,是对\(Y \left. \right| \mu \sim {\mathcal {S}} 的预测密度进行贝叶斯估计。\right| \mu \sim {\mathcal {S}}{mathcal {N}}_p (\mu , v_y I_p, \lambda )\) under Kullback-Leibler loss, based on \(X (left.\right| \mu \sim {\mathcal {S}}{mathcal {N}}_p (\mu , v_x I_p, \lambda )\) 与已知的依赖性和偏度参数。我们得到了贝叶斯预测密度的表示方法,包括最小风险等变预测密度(\hat{p}_{\pi _{o}}\),它是相对于非信息先验的贝叶斯预测密度(\pi _0\equiv 1\)。George 等人(Ann Stat 34:78-91, 2006)利用点估计问题与预测密度估计问题之间的平行关系,在这两个问题的风险差异之间建立了联系。类似联系的发展使我们能够确定支配(\hat{p}_{pi _{o}})和最小性的充分条件。首先,我们证明了\(hat{p}_{pi _{o}}/)是偏正态模型 KL 风险下的最小预测密度。之后,对于偏度参数值较小的偏正态分布子类,我们得到了贝叶斯最小密度的类别,这些密度在KL损失下改善了\(\hat{p}_{pi _{o}}\)。此外,对于维数 \(p\ge 4\), 我们得到了贝叶斯最小密度的类别,这些密度在 KL 损失下改善了整个偏态正态分布类别的 \(hat{p}_{pi _{o}}\) 。当\(p\ge 5\) 时,构建了适当先验(包括广义学生先验)的例子,这些先验产生了贝叶斯最小密度,在KL损失下改善了\(\hat{p}_{pi _{o}}\)。这一发现是Liang和Barron(IEEE Trans Inf Theory 50(11):2708-2726,2004)、George等人(Ann Stat 34:78-91,2006)和Komaki(Biometrika 88(3):859-864,2001)的结果在非对称分布子类上的扩展。
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来源期刊
Metrika
Metrika 数学-统计学与概率论
CiteScore
1.50
自引率
14.30%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.
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