Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis

@article{Kirch2017BeyondWN,
  title={Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis},
  author={Claudia Kirch and M. Edwards and Alexander Meier and R. Meyer},
  journal={Bayesian Analysis},
  year={2017},
  volume={14},
  pages={1037-1073}
}
  • Claudia Kirch, M. Edwards, +1 author R. Meyer
  • Published 2017
  • Mathematics
  • Bayesian Analysis
  • 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… CONTINUE READING
    10 Citations

    Figures and Tables from this paper.

    Bayesian nonparametric spectral density estimation using B-spline priors
    • 14
    • PDF
    Adaptive Bayesian Time–Frequency Analysis of Multivariate Time Series
    • Zeda Li, R. Krafty
    • Computer Science, Medicine
    • Journal of the American Statistical Association
    • 2019
    • 9
    Spectral density estimation using P-spline priors
    • 1
    • Highly Influenced
    Bayesian spectral density estimation using P-splines with quantile-based knot placement
    • 2
    • Highly Influenced
    • PDF
    Identifying and Addressing Nonstationary LISA Noise
    • 1
    • PDF

    References

    SHOWING 1-10 OF 100 REFERENCES
    Semiparametric Bayesian Inference for Time Series with Mixed Spectra
    • 62
    • Highly Influential
    Bayesian Estimation of the Spectral Density of a Time Series
    • 97
    • Highly Influential
    • PDF
    A frequency domain bootstrap for Whittle estimation under long-range dependence
    • 16
    Bayesian nonparametric spectral density estimation using B-spline priors
    • 14
    • PDF
    Computational Aspects of Bayesian Spectral Density Estimation
    • 6
    • Highly Influential
    • PDF