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Multiple Hypothesis Testing-Based Cepstrum Thresholding for Nonparametric Spectral Estimation
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DOI：10.1109/LSP.2022.3222949.png)
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Abstract
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In this letter we revisit the problem of smoothed nonparametric spectral estimation via cepstrum thresholding. We formulate the problem of cepstrum thresholding as a multiple hypothesis testing problem and use the false discovery rate (FDR) and familywise error rate (FER) procedures to threshold the cepstral coefficients. We compare the FDR and FER approaches with a previously proposed individual hypothesis testing approach and show that the cepstrum thresholding based on FDR and FER can yield spectral estimates with lower mean square error (MSE).
Keywords:
Nonparametric spectral estimation
cepstral coefficients
FDR
FER
periodogram
multiple hypothesis testing
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