Journal ArticleDOI
Detection of non-Gaussian signals in non-Gaussian noise using the bispectrum
Melvin J. Hinich,Gary R. Wilson +1 more
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TLDR
It is concluded that the bispectrum can be used effectively to detect non-Gaussian signals in the presence of interfering noise and that it may perform better, depending on the degree of non- Gaussianity, than energy detection.Abstract:
The problem of detecting a non-Gaussian time series in the presence of additive Gaussian or non-Gaussian noise is cast into a classical hypothesis testing framework, using the sample bispectrum as the test statistic The power of the test is demonstrated as a function of signal-to-noise ratio, the degree of skewness of the signal, and processing parameters The results are compared to the power of a classical energy detection test It is concluded that the bispectrum can be used effectively to detect non-Gaussian signals in the presence of interfering noise and that it may perform better, depending on the degree of non-Gaussianity, than energy detection >read more
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Short-term load forecasting via ARMA model identification including non-Gaussian process considerations
Shyh-Jier Huang,Kuang-Rong Shih +1 more
TL;DR: The concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process considerations, and with embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately.
Journal ArticleDOI
On inference for partially observed nonlinear diffusion models using the Metropolis–Hastings algorithm
Gareth O. Roberts,O. Stramer +1 more
TL;DR: A new Markov chain Monte Carlo approach to Bayesian analysis of discretely observed diffusion processes and shows that, because of full dependence between the missing paths and the volatility of the diffusion, the rate of convergence of basic algorithms can be arbitrarily slow if the amount of the augmentation is large.
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Atmospheric effects on InSAR measurements and their mitigation
TL;DR: This paper provides a systematic review of the work carried out in this area of atmospheric effects on repeat-pass InSAR and reviews the methods developed for mitigating the atmospheric effects.
Journal ArticleDOI
Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods
TL;DR: The monitoring methods discussed in this paper include pattern recognition, fuzzy systems, decision trees, expert systems and neural networks, which are suitable for automated monitoring of manufacturing processes.
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Bibliography on higher-order statistics
References
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Journal ArticleDOI
Bispectrum estimation: A digital signal processing framework
TL;DR: In this article, the authors place bispectrum estimation in a digital signal processing framework in order to aid engineers in grasping the utility of the available bispectral estimation techniques, and discuss application problems that can directly benefit from the use of the Bispectrum, and to motivate research in this area.
Journal ArticleDOI
Digital Bispectral Analysis and Its Applications to Nonlinear Wave Interactions
Young C. Kim,Edward J. Powers +1 more
TL;DR: The bispectrum, which is an ensemble average of a product of three spectral components, is shown to be a very useful diagnostic tool in experimental studies of nonlinear wave interactions in random media.
Journal ArticleDOI
Testing for Gaussianity and Linearity of a Stationary Time Series.
TL;DR: In this paper, a simple estimator of the bispectrum, the Fourier transform of (sub c xxx (m,n)) is used to construct a statistic to test whether the bisensor of (x(t)) is non-zero.
Book ChapterDOI
Deconvolution and Estimation of Transfer Function Phase and Coefficients for NonGaussian Linear Processes.
Keh-Shin Lii,Murray Rosenblatt +1 more
TL;DR: In this paper, it is shown that the phase of the transfer function can be estimated under broad conditions and the asymptotic behavior of a phase estimate is determined under broad assumptions.
Journal ArticleDOI
Evidence of Nonlinearity in Daily Stock Returns
TL;DR: In this paper, the authors apply a newly developed statistical technique to time series of daily rates of return of 15 common stocks and show that the results suggest that daily stock returns are generated by a nonlinear process.