Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis
arXiv:1701.04846
Abstract
The Whittle likelihood is widely used for Bayesian nonparametric estimation of the spectral density of stationary time series. However, the loss of efficiency for non-Gaussian time series can be substantial. On the other hand, parametric methods are more powerful if the model is well-specified, but may fail entirely otherwise. Therefore, we suggest a nonparametric correction of a parametric likelihood taking advantage of the efficiency of parametric models while mitigating sensitivities through a nonparametric amendment. Using a Bernstein-Dirichlet prior for the nonparametric spectral correction, we show posterior consistency and illustrate the performance of our procedure in a simulation study and with LIGO gravitational wave data.
References in corpus (7)
- Properties of the Binary Black Hole Merger GW150914
- BayesLine: Bayesian Inference for Spectral Estimation of Gravitational Wave Detector Noise
- TFT-bootstrap: Resampling time series in the frequency domain to obtain replicates in the time domain
- Modelling coloured residual noise in gravitational-wave signal processing
- Bayesian nonparametric spectral density estimation using B-spline priors
- Fortifying the characterization of binary mergers in LIGO data
- Bayesian semiparametric power spectral density estimation with applications in gravitational wave data analysis