Rosen Ori, Thompson Wesley K
Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.
Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Comput Stat Data Anal. 2009 Sep 1;53(11):3773-3786. doi: 10.1016/j.csda.2009.03.026. Epub 2009 Apr 8.
In this paper we present a model for the analysis of multivariate functional data with unequally spaced observation times that may differ among subjects. Our method is formulated as a Bayesian mixed-effects model in which the fixed part corresponds to the mean functions, and the random part corresponds to individual deviations from these mean functions. Covariates can be incorporated into both the fixed and the random effects. The random error term of the model is assumed to follow a multivariate Ornstein-Uhlenbeck process. For each of the response variables, both the mean and the subject-specific deviations are estimated via low-rank cubic splines using radial basis functions. Inference is performed via Markov chain Monte Carlo methods.
在本文中，我们提出了一个用于分析具有不等间距观测时间的多变量函数数据的模型，这些观测时间可能因个体而异。我们的方法被构建为一个贝叶斯混合效应模型，其中固定部分对应于均值函数，随机部分对应于与这些均值函数的个体偏差。协变量可以纳入固定效应和随机效应中。该模型的随机误差项假定遵循多元奥恩斯坦 - 乌伦贝克过程。对于每个响应变量，均值和个体特定偏差均通过使用径向基函数的低秩三次样条进行估计。通过马尔可夫链蒙特卡罗方法进行推断。
