Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in a wide range of applications.
In this book, the authors present the recently developed nonparametric adaptive filtering based algorithms for the missing-data case, namely gapped-data APES (GAPES) and the more general missing-data APES (MAPES).

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