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Aggregation of Spectral Density Estimators
"... Given stationary time series data, we study the problem of finding the best linear combination of a set of lag window spectral density estimators with respect to the mean squared risk. We present an aggregation procedure and prove a sharp oracle inequality for its risk. We also provide simulations d ..."
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Given stationary time series data, we study the problem of finding the best linear combination of a set of lag window spectral density estimators with respect to the mean squared risk. We present an aggregation procedure and prove a sharp oracle inequality for its risk. We also provide simulations
Monotone spectral density estimation
, 2009
"... We propose two estimators of a unimodal or monotone spectral density, that are based on the periodogram. These are the isotonic regression of the periodogram and the isotonic regression of the logperiodogram. We derive pointwise limit distribution results for the proposed estimators for short memor ..."
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Cited by 8 (0 self)
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We propose two estimators of a unimodal or monotone spectral density, that are based on the periodogram. These are the isotonic regression of the periodogram and the isotonic regression of the logperiodogram. We derive pointwise limit distribution results for the proposed estimators for short
Noise power spectral density estimation based on optimal smoothing and minimum statistics
 IEEE TRANS. SPEECH AND AUDIO PROCESSING
, 2001
"... We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a ..."
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Cited by 267 (7 self)
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We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use
Automatic Local Smoothing for Spectral Density Estimation
 Scandinavian Journal of Statistics
, 1998
"... This article uses local polynomial techniques to fit Whittle's likelihood for spectral density estimation. Asymptotic sampling properties of the proposed estimators are derived, and adaptation of the proposed estimator to the boundary effect is noted. We show that the Whittle likelihood based e ..."
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Cited by 18 (2 self)
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This article uses local polynomial techniques to fit Whittle's likelihood for spectral density estimation. Asymptotic sampling properties of the proposed estimators are derived, and adaptation of the proposed estimator to the boundary effect is noted. We show that the Whittle likelihood based
Spectral Density Estimation via Wavelet Shrinkage
, 1996
"... We study the problem of estimating the spectral density of a stationary Gaussian time series. We use an orthogonal wavelet system whose members are periodic functions and have a finite number of nonzero Fourier coefficients  periodized Meyer wavelets. We apply shrinkage rules to the empirical wav ..."
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Cited by 4 (0 self)
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We study the problem of estimating the spectral density of a stationary Gaussian time series. We use an orthogonal wavelet system whose members are periodic functions and have a finite number of nonzero Fourier coefficients  periodized Meyer wavelets. We apply shrinkage rules to the empirical
doi:10.1017/S026646660999051X ASYMPTOTICS OF SPECTRAL DENSITY ESTIMATES
"... We consider nonparametric estimation of spectral densities of stationary processes, a fundamental problem in spectral analysis of time series. Under natural and easily verifiable conditions, we obtain consistency and asymptotic normality of spectral density estimates. Asymptotic distribution of maxi ..."
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Cited by 3 (1 self)
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We consider nonparametric estimation of spectral densities of stationary processes, a fundamental problem in spectral analysis of time series. Under natural and easily verifiable conditions, we obtain consistency and asymptotic normality of spectral density estimates. Asymptotic distribution
Rate Of Convergence For Logspline Spectral Density Estimation
 Journal of Time Series Analysis
, 1994
"... . The logarithm of the spectral density function for a stationary process is approximated by polynomial splines. The approximation is chosen to maximize the expected loglikelihood based on the asymptotic properties of the periodogram. Estimates of this approximation are shown to possess the usual n ..."
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Cited by 12 (2 self)
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. The logarithm of the spectral density function for a stationary process is approximated by polynomial splines. The approximation is chosen to maximize the expected loglikelihood based on the asymptotic properties of the periodogram. Estimates of this approximation are shown to possess the usual
Testing for trend stationarity using spectral density estimators. manuscript
"... We propose a new test statistic for trend stationarity against difference stationarity using spectral density estimators. The spectral density of the first differenced process equals to zero at the zero frequency under the null of trend stationarity, whereas difference stationarity yields positive s ..."
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Cited by 1 (0 self)
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We propose a new test statistic for trend stationarity against difference stationarity using spectral density estimators. The spectral density of the first differenced process equals to zero at the zero frequency under the null of trend stationarity, whereas difference stationarity yields positive
COMPUTATIONAL ASPECTS OF BAYESIAN SPECTRAL DENSITY ESTIMATION
, 2012
"... Abstract. Gaussian timeseries models are often specified through their spectral density. Such models pose several computational challenges, in particular because of the nonsparse nature of the covariance matrix. We derive a fast approximation of the likelihood for such models. We use importance sa ..."
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Abstract. Gaussian timeseries models are often specified through their spectral density. Such models pose several computational challenges, in particular because of the nonsparse nature of the covariance matrix. We derive a fast approximation of the likelihood for such models. We use importance
Results 1  10
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1,553,780