Multiple Hypothesis Testing-Based Cepstrum Thresholding for Nonparametric Spectral Estimation

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).

Automated summary

In this study, the problem of smoothed nonparametric spectral estimation via cepstrum thresholding is revisited. The cepstrum thresholding problem is formulated as a multiple hypothesis testing problem, and the false discovery rate (FDR) and familywise error rate (FER) procedures are used to threshold the cepstral coefficients. The FDR and FER approaches are compared with a previously proposed individual hypothesis testing approach, and it is shown that cepstrum thresholding based on FDR and FER can yield spectral estimates with lower mean square error (MSE).