[[abstract]]It is well-known that the linear prediction (LP) spectral estimator is equivalent to the maximum entropy spectral estimator and they are also equivalent to the maximum spectral flatness spectral estimator for AR processes of known order. The author proposes a new higher-order statistics (HOS) based linear prediction error (LPE) filter. The author also presents LP polyspectral estimator, maximum polyspectral flatness polyspectral estimator, maximum higher-order entropy polyspectral estimator and equivalencies on these polyspectral estimators. The results presented provide some theoretical foundations on the polyspectral estimation and modeling of non-Gaussian AR processes[[fileno]]2030157030079[[department]]電機工程學
When a dataset is corrupted by noise, the model for data generating process is misspecified and can ...
A new class of large-sample covariance and spectral density matrix estimators is proposed based on t...
We introduce a new perspective on spectral dimensionality reduction which views these methods as Gau...
[[abstract]]© 1993 Institute of Electrical and Electronics Engineers-The authors present a theoretic...
A new spectral estimate, the maximum entropy method, is described. In the maximum entropy method, th...
[[abstract]]This criterion requires only partial Mth-order cumulants CM,e(0,k1, k1, . . ., kM/2-1, k...
[[abstract]]The authors propose two criteria for the design of (minimum-phase) linear prediction err...
[[abstract]]Chi (see Sixth European Signal Processing Conference, EUSIPCO-92, Belgium, p.755-8, vol....
Several algorithms have been used in maximum entropy spectral analysis. Among them, the standard Bur...
Abstract—Structured covariances occurring in spectral analysis, filtering and identification need to...
The eigenfunctions and eigenvectors of the operators of autoregression and of sliding mean are defin...
This thesis is concerned with the extension of the theory and computational techniques of time-serie...
Improved performance in higher-order spectral density estimation (polyspectral estimation) and densi...
Traditional maximum entropy spectral estimation determines a power spectrum from covariance estimate...
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian...
When a dataset is corrupted by noise, the model for data generating process is misspecified and can ...
A new class of large-sample covariance and spectral density matrix estimators is proposed based on t...
We introduce a new perspective on spectral dimensionality reduction which views these methods as Gau...
[[abstract]]© 1993 Institute of Electrical and Electronics Engineers-The authors present a theoretic...
A new spectral estimate, the maximum entropy method, is described. In the maximum entropy method, th...
[[abstract]]This criterion requires only partial Mth-order cumulants CM,e(0,k1, k1, . . ., kM/2-1, k...
[[abstract]]The authors propose two criteria for the design of (minimum-phase) linear prediction err...
[[abstract]]Chi (see Sixth European Signal Processing Conference, EUSIPCO-92, Belgium, p.755-8, vol....
Several algorithms have been used in maximum entropy spectral analysis. Among them, the standard Bur...
Abstract—Structured covariances occurring in spectral analysis, filtering and identification need to...
The eigenfunctions and eigenvectors of the operators of autoregression and of sliding mean are defin...
This thesis is concerned with the extension of the theory and computational techniques of time-serie...
Improved performance in higher-order spectral density estimation (polyspectral estimation) and densi...
Traditional maximum entropy spectral estimation determines a power spectrum from covariance estimate...
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian...
When a dataset is corrupted by noise, the model for data generating process is misspecified and can ...
A new class of large-sample covariance and spectral density matrix estimators is proposed based on t...
We introduce a new perspective on spectral dimensionality reduction which views these methods as Gau...