A Bayesian Multivariate Functional Dynamic Linear Model

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 fr...

Multivariate Stochastic Volatility with Bayesian Dynamic Linear Models

This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distributions. A diagonal matrix of discount factors is employed in order to discount...

A Bayesian regression model for multivariate functional data

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 ...

Bayesian Model Averaging of Dynamic Linear Models

In this paper we aim to compare the performance of three different Bayesian model averaging (or mixture) methods applied to regression dynamic linear models for beverage data from Zimbabwe. The models are chosen to reflect different plausible causal structures of association between beverage sales and other variables, thought to influence beverage sales, such as prices, temperature and maize cr...

Bayesian regression with multivariate linear splines