1,214 research outputs found
ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data
There are many different ways in which change point analysis can be
performed, from purely parametric methods to those that are distribution free.
The ecp package is designed to perform multiple change point analysis while
making as few assumptions as possible. While many other change point methods
are applicable only for univariate data, this R package is suitable for both
univariate and multivariate observations. Estimation can be based upon either a
hierarchical divisive or agglomerative algorithm. Divisive estimation
sequentially identifies change points via a bisection algorithm. The
agglomerative algorithm estimates change point locations by determining an
optimal segmentation. Both approaches are able to detect any type of
distributional change within the data. This provides an advantage over many
existing change point algorithms which are only able to detect changes within
the marginal distributions
A Bayesian Multivariate Functional Dynamic Linear Model
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
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