Topic
Spectral density estimation
About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In short-time spectral estimation, Sacchi et al. derived new nonlinear spectral estimators defined as minimizers of penalized criteria, and it is shown that IRLS is a block-coordinate descent (BCD) method performing the minimization of a half-quadratic(HQ) energy.
31 citations
TL;DR: A new bandwidth selection method that is based on a coupling of the so-called plug-in and the unbiased risk estimation ideas is proposed, which often outperforms some other commonly used bandwidth selection methods.
31 citations
TL;DR: In this article, the authors show that the appropriate selection of constraints in the variational formulation of spectral estimation leads to new models and more freedom for the designer, and illustrate the relationships between the set of constraints and the underlying model in the procedure.
Abstract: In this paper we show that the appropriate selection of constraints in the variational formulation of spectral estimation leads to new models and more freedom for the designer. We also illustrate the relationships between the set of constraints and the underlying model in the procedure. The rest of the paper concentrates on obtaining maximum entropy ARMA models for spectral estimation, using cepstral constraints and correlation constraints simultaneously. The nonlinearity that these kinds of constraints introduce is avoided by a simple linearization, that provides an estimator which is easily implemented. Finally, some examples are given to illustrate the performance of the proposed method.
31 citations
TL;DR: In this article, a nonparametric prior based on a mixture of B-spline distributions is specified and can be regarded as a generalization of the Bernstein polynomial prior of Petrone (1999a,b) and Choudhuri et al. (2004).
Abstract: We present a new Bayesian nonparametric approach to estimating the spectral density of a stationary time series. A nonparametric prior based on a mixture of B-spline distributions is specified and can be regarded as a generalization of the Bernstein polynomial prior of Petrone (1999a,b) and Choudhuri et al. (2004). Whittle's likelihood approximation is used to obtain the pseudo-posterior distribution. This method allows for a data-driven choice of the number of mixture components and the location of knots. Posterior samples are obtained using a Metropolis-within-Gibbs Markov chain Monte Carlo algorithm, and mixing is improved using parallel tempering. We conduct a simulation study to demonstrate that for complicated spectral densities, the B-spline prior provides more accurate Monte Carlo estimates in terms of $L_1$-error and uniform coverage probabilities than the Bernstein polynomial prior. We apply the algorithm to annual mean sunspot data to estimate the solar cycle. Finally, we demonstrate the algorithm's ability to estimate a spectral density with sharp features, using real gravitational wave detector data from LIGO's sixth science run, recoloured to match the Advanced LIGO target sensitivity.
30 citations
02 Apr 1979
TL;DR: The technique consists of extrapolating observed data beyond the observation window by means of an autoregressive data-generation model and high-resolution spectral analyses are obtained by conventional discrete Fourier transforms of the extrapolated data.
Abstract: Until recently most high-resolution autoregressive spectral analysis techniques have been applied to analysis of either single-channel waveforms or multichannel vector processes. However, the use of data prediction autoregressive spectral analysis permits the application of some high-resolution single-channel methods to high-resolution spectral analysis of data fields in two or more dimensions. The technique consists of extrapolating observed data beyond the observation window by means of an autoregressive data-generation model. High-resolution spectral analyses are then obtained by conventional discrete Fourier transforms (DFTs) of the extrapolated data.
30 citations