Ultra-efficient MCMC for Bayesian longitudinal functional data analysis

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-06-07 DOI:10.1080/10618600.2024.2362227

Thomas Y. Sun, Daniel R. Kowal

引用次数: 0

Abstract

Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bay...

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用于贝叶斯纵向功能数据分析的超高效 MCMC

函数混合模型对于依赖函数数据的回归分析非常有用，包括带有标量预测因子的纵向函数数据。然而，现有的贝叶斯混合模型算法并不能解决这些问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.