A Bayesian Multivariate Functional Dynamic Linear Model

作者：Kowal Daniel R^*; Matteson David S; Ruppert David

来源：Journal of the American Statistical Association, 2017, 112(518): 733-744.

DOI：10.1080/01621459.2016.1165104

• Hierarchical Bayes
• Orthogonality constraint
• Spline
• Time-frequency analysis
• Yield curve

We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data functional, time dependent, and multivariate components we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework. The proposed methods identify a time-invariant functional basis for the functional observations, which is smooth and interpretable, and can be made common across multivariate observations for additional information sharing. The Bayesian framework permits joint estimation of the model parameters, provides exact inference (up to MCMC error) on specific parameters, and allows generalized dependence structures. Sampling from the posterior distribution is accomplished with an efficient Gibbs sampling algorithm. We illustrate the proposed framework with two applications: (1) multi-economy yield curve data from the recent global recession, and (2) local field potential brain signals in rats, for which we develop a multivariate functional time series approach for multivariate time-frequency analysis. Supplementary, materials, including R code and the multi-economy yield curve data, are available online.

• 出版日期2017-6