Adaptive Bayesian sum of trees model for covariate-dependent spectral analysis.
Adaptive Bayesian sum of trees model for covariate-dependent spectral analysis.
复制标题
用于协变量相关谱分析的自适应贝叶斯树和模型。
DOI：
10.1111/biom.13763
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发表时间：
2023
期刊：
影响因子：
1.9
通讯作者：
Bruce,ScottA
中科院分区：
文献类型：
--
作者：
Wang,Yakun;Li,Zeda;Bruce,ScottA
本文介绍了一种灵活的自适应非参数估计方法，用于估计多时间序列的多协变量与功率谱之间的关联。该方法使用贝叶斯树和模型来捕获协变量和功率谱之间的复杂依赖关系和相互作用，这在生物医学时间序列的研究中经常观察到。利用贝叶斯惩罚线性样条对树内终端节点对应的局部功率谱进行非参数估计。这些树被认为是随机的，并使用贝叶斯反拟合马尔可夫链蒙特卡罗（MCMC）算法进行拟合，该算法通过可逆跳跃MCMC技术顺序考虑树的修改。对于高维协变量，考虑了树分裂比例的稀疏性诱导Dirichlet超先验，提供了协变量效应的稀疏估计和有效的变量选择。通过对树的后验分布进行平均，该方法可以恢复多个协变量间功率谱的平稳变化和突变变化。通过模拟来评估经验性能，以证明所提出的方法能够准确地恢复复杂的关系和相互作用。该方法通过评估在其他协变量存在下步幅间隔时间序列功率谱的年龄相关变化来研究幼儿的步态成熟。
This paper introduces a flexible and adaptive nonparametric method for estimating the association between multiple covariates and power spectra of multiple time series. The proposed approach uses a Bayesian sum of trees model to capture complex dependencies and interactions between covariates and the power spectrum, which are often observed in studies of biomedical time series. Local power spectra corresponding to terminal nodes within trees are estimated nonparametrically using Bayesian penalized linear splines. The trees are considered to be random and fit using a Bayesian backfitting Markov chain Monte Carlo (MCMC) algorithm that sequentially considers tree modifications via reversible-jump MCMC techniques. For high-dimensional covariates, a sparsity-inducing Dirichlet hyperprior on tree splitting proportions is considered, which provides sparse estimation of covariate effects and efficient variable selection. By averaging over the posterior distribution of trees, the proposed method can recover both smooth and abrupt changes in the power spectrum across multiple covariates. Empirical performance is evaluated via simulations to demonstrate the proposed method's ability to accurately recover complex relationships and interactions. The proposed methodology is used to study gait maturation in young children by evaluating age-related changes in power spectra of stride interval time series in the presence of other covariates.