beyondWhittle-package {beyondWhittle}R Documentation

Bayesian spectral inference for stationary time series

Description

Bayesian parametric, nonparametric and semiparametric procedures for spectral density inference of univariate and multivariate time series

Details

The package contains several methods (parametric, nonparametric and semiparametric) for Bayesian spectral density inference. The main algorithms to fit the models for univariate time series are:

The package also contains the following models for multivariate time series:

as well as some useful utility functions. To get started, it is recommended to consider the examples and documentation of the functions listed above. The work was supported by DFG grant KI 1443/3-1.

Author(s)

Claudia Kirch, Renate Meyer, Matthew C. Edwards, Alexander Meier

Maintainer: Alexander Meier <meier.alexander@posteo.de>

References

N. Choudhuri, S. Ghosal and A. Roy (2004) Bayesian estimation of the spectral density of a time series JASA <doi:10.1198/016214504000000557>

C. Kirch, M. C. Edwards, A. Meier and R. Meyer (2018) Beyond Whittle: Nonparametric Correction of a Parametric Likelihood with a Focus on Bayesian Time Series Analysis Bayesian Analysis <doi:10.1214/18-BA1126>

A. Meier (2018) A matrix Gamma process and applications to Bayesian analysis of multivariate time series PhD thesis, OvGU Magdeburg <doi:10.25673/13407>


[Package beyondWhittle version 1.1.1 Index]