Various robust modifications of the classical methods of power spectra esti- mation, both nonparametric and parametric, are considered. Their performance evaluation is studied in autoregressive models with contamination. It is found out that prospective robust estimates of power spectra are based on robust highly efficient estimates of autocovariances and on robust filtering algorithms. Several open problems for future research are formulated
Utilizing time series modeling entails estimating the model parameters and dispersion. Classical est...
AbstractThree methods of estimating the parameters of a power spectrum are analyzed. The three metho...
The problem of estimating the power spectrum from noisy autocorrelation values is considered in this...
<p>Various robust versions of the classical methods of power spectra estimation are considered.<br /...
The power spectrum is a commonly used tool when analyzing time series in the frequency domain. It ca...
In this study, robust nonparametric spectral estimation methods for non-Gaussian environments are pr...
In this study, robust nonparametric spectral estimation methods for non-Gaussian environments are pr...
When a dataset is corrupted by noise, the model for data generating process is misspecified and can ...
Robust estimation of power spectra, coherences, and transfer functions is investigated in the contex...
A new time-varying autoregressive modeling has been proposed as a tool for time-frequency analysis o...
In this chapter we consider a class of parametric spectrum esti- mators based on a generalized linea...
This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on th...
Several algorithms have been used in maximum entropy spectral analysis. Among them, the standard Bur...
Spectral classification is a commonly used technique for discriminating between two or more signals....
The problem of estimating the power spectrum from noisy autocorrelation values is considered in this...
Utilizing time series modeling entails estimating the model parameters and dispersion. Classical est...
AbstractThree methods of estimating the parameters of a power spectrum are analyzed. The three metho...
The problem of estimating the power spectrum from noisy autocorrelation values is considered in this...
<p>Various robust versions of the classical methods of power spectra estimation are considered.<br /...
The power spectrum is a commonly used tool when analyzing time series in the frequency domain. It ca...
In this study, robust nonparametric spectral estimation methods for non-Gaussian environments are pr...
In this study, robust nonparametric spectral estimation methods for non-Gaussian environments are pr...
When a dataset is corrupted by noise, the model for data generating process is misspecified and can ...
Robust estimation of power spectra, coherences, and transfer functions is investigated in the contex...
A new time-varying autoregressive modeling has been proposed as a tool for time-frequency analysis o...
In this chapter we consider a class of parametric spectrum esti- mators based on a generalized linea...
This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on th...
Several algorithms have been used in maximum entropy spectral analysis. Among them, the standard Bur...
Spectral classification is a commonly used technique for discriminating between two or more signals....
The problem of estimating the power spectrum from noisy autocorrelation values is considered in this...
Utilizing time series modeling entails estimating the model parameters and dispersion. Classical est...
AbstractThree methods of estimating the parameters of a power spectrum are analyzed. The three metho...
The problem of estimating the power spectrum from noisy autocorrelation values is considered in this...