Contiguity of the Whittle measure for a Gaussian time series

Choudhuri, Nidhan; Ghosal, Subhashis

doi:10.1093/biomet/91.1.211

Cited by 36 publications

(33 citation statements)

References 26 publications

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“…The difference between the exact and approximate ("Whittle") model is explicated in more detail in [30].…”

Section: The Normal Modelmentioning

confidence: 99%

“…The above time domain expression sheds some light on the exact shortcomings of the Whittle likelihood approximation; you can see for example that it will be a poor approximation in case of predominantly low-frequency noise and a small number of observations. The problem may be tackled via windowing of the data, or it will also lessen with an increasing sample size; again, the features of this approximation are discussed in more detail in [30]. …”

Section: The Corresponding Time-domain Modelmentioning

confidence: 99%

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Modelling coloured residual noise in gravitational-wave signal processing

Röver

Meyer

Christensen

2010

Class. Quantum Grav.

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Abstract. We introduce a signal processing model for signals in non-white noise, where the exact noise spectrum is a priori unknown. The model is based on a Student's t distribution and constitutes a natural generalization of the widely used normal (Gaussian) model. This way, it allows for uncertainty in the noise spectrum, or more generally is also able to accommodate outliers (heavy-tailed noise) in the data. Examples are given pertaining to data from gravitational wave detectors.

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“…The difference between the exact and approximate ("Whittle") model is explicated in more detail in [30].…”

Section: The Normal Modelmentioning

confidence: 99%

Section: The Corresponding Time-domain Modelmentioning

confidence: 99%

Modelling coloured residual noise in gravitational-wave signal processing

Röver

Meyer

Christensen

2010

Class. Quantum Grav.

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“…[9] For a stationary time series L(q) is computed on the spectral density [see, e.g., Beran, 1994;Montanari et al, 2000;Chouduri et al, 2004]. In practice, the model calibration is carried out essentially by matching the spectral densities of model output and river flow process.…”

Section: Approximation Proposed By Whittle To the Gaussian Maximum LImentioning

confidence: 99%

Calibration of hydrological models in the spectral domain: An opportunity for scarcely gauged basins?

Montanari

Toth

2007

Water Resources Research

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[1] This study considers the use of the maximum likelihood estimator proposed by Whittle for calibrating the parameters of hydrological models. Whittle's likelihood provides asymptotically consistent estimates for Gaussian and non-Gaussian data, even in the presence of long-range dependence. This method may represent a valuable opportunity in the context of ungauged or scarcely gauged catchments. In fact, the only information required for model parameterization is essentially the spectral density function of the actual process simulated by the model. When long series of calibration data are not available, the spectral density can be inferred by using old and sparse records, regionalization methods, or information on the correlation properties of the process itself. The proposed procedure is applied to the case studies of two Italian river basins, for which a lumped rainfall-runoff model has been calibrated by emulating scarcely gauged situations. It is shown that the Whittle estimator can be applied in such context with satisfactory results.Citation: Montanari, A., and E. Toth (2007), Calibration of hydrological models in the spectral domain: An opportunity for scarcely gauged basins?, Water Resour. Res., 43, W05434,

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“…Using the contiguity result of Choudhuri et al (2004c), the following result was shown by Choudhuri et al (2004a) under the above assumptions.…”

Section: Bernstein Polynomial Priormentioning

confidence: 95%