Hierarchical Bayesian models for predicting spatially correlated curves
Hierarchical Bayesian models for predicting spatially correlated curves
复制标题
用于预测空间相关曲线的分层贝叶斯模型
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
B. Mallick
中科院分区：
文献类型：
--
作者：
J. Song;B. Mallick
摘要函数数据分析是统计学研究的一个新领域，有着广泛的应用。在本文中，我们提出了基于小波的空间相关函数数据的新模型。这些模型使人们能够将空间上观测到的曲线规则化，并预测未观测到的地点的曲线。我们使用后验预测准则比较了这些贝叶斯模型和几种先验在小波系数上的性能。通过对孔隙度数据的分析，说明了所提出的模型。
ABSTRACT Functional data analysis has emerged as a new area of statistical research with a wide range of applications. In this paper, we propose novel models based on wavelets for spatially correlated functional data. These models enable one to regularize curves observed over space and predict curves at unobserved sites. We compare the performance of these Bayesian models with several priors on the wavelet coefficients using the posterior predictive criterion. The proposed models are illustrated in the analysis of porosity data.