Autoregression and irregular sampling: spectral estimation
Signal Processing
A consistent nonparametric spectral estimator for randomly sampled signals
IEEE Transactions on Signal Processing
Random sampling of deterministic signals: statistical analysis of Fourier transform estimates
IEEE Transactions on Signal Processing
Autoregressive spectral analysis when observations are missing
Automatica (Journal of IFAC)
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Spectral preprocessing for clustering time-series gene expressions
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Spectral analysis of nouniformly sampled data: a new approach versus the periodogram
IEEE Transactions on Signal Processing
Spectral analysis of nonuniformly sampled data -- a review
Digital Signal Processing
Digital Signal Processing
Consistent estimation of non-bandlimited spectral density from uniformly spaced samples
IEEE Transactions on Information Theory
Anomaly detection in noisy and irregular time series: the "turbodiesel charging pressure" case study
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Passive online RTT estimation for flow-aware routers using one-way traffic
NETWORKING'10 Proceedings of the 9th IFIP TC 6 international conference on Networking
Spectral estimation for locally stationary time series with missing observations
Statistics and Computing
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The spectral analysis of regularly-sampled (RS) data is a well-established topic, and many useful methods are available for performing it under different sets of conditions. The same cannot be said about the spectral analysis of irregularly-sampled (IS) data: despite a plethora of published works on this topic, the choice of a spectral analysis method for IS data is essentially limited, on either technical or computational grounds, to the periodogram and its variations. In our opinion this situation is far from satisfactory, given the importance of the spectral analysis of IS data for a considerable number of applications in such diverse fields as engineering, biomedicine, economics, astronomy, seismology, and physics, to name a few. In this paper we introduce a number of IS data approaches that parallel the methods most commonly used for spectral analysis of RS data: the periodogram (PER), the Capon method (CAP), the multiple-signal characterization method (MUSIC), and the estimation of signal parameters via rotational invariance technique (ESPRIT). The proposed IS methods are as simple as their RS counterparts, both conceptually and computationally. In particular, the fast algorithms derived for the implementation of the RS data methods can be used mutatis mutandis to implement the proposed parallel IS methods. Moreover, the expected performance-based ranking of the IS methods is the same as that of the parallel RS methods: all of them perform similarly on data consisting of well-separated sinusoids in noise, MUSIC and ESPRIT outperform the other methods in the case of closely-spaced sinusoids in white noise, and CAP outperforms PER for data whose spectrum has a small-to-medium dynamic range (MUSIC and ESPRIT should not be used in the latter case).