Forward-only and forward-backward sample covariances – A comparative study

Jansson, Magnus; Stoica, Petre

doi:10.1016/s0165-1684(99)00037-7

Cited by 46 publications

(15 citation statements)

References 6 publications

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“…We also define matrix similar to that in (22) but with determined from (13) and Lemma 2 for the case when . Another difference from (12) is the number of terms in (26) which is essentially smaller than in (11) and (12), namely (27) These conditions lead to the accuracy associated with the model, as follows. Corollary 1: The error associated with the -causal model with is given by (28) Proof: The proof follows directly from the above.…”

Section: Rigorous Statement Of Problemmentioning

confidence: 99%

Best causal mathematical models for a nonlinear system

Torokhti

Howlett

Pearce

2005

IEEE Trans. Circuits Syst. I

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Section: Rigorous Statement Of Problemmentioning

confidence: 99%

Best causal mathematical models for a nonlinear system

Torokhti

Howlett

Pearce

2005

IEEE Trans. Circuits Syst. I

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“…In order to exploit the persymmetry of , we need to use the forwardbackward (FB) log likelihood [32], which is a combination of the forward-looking and the backward-looking loglikelihood functions. The forward-looking log-likelihood log f z (Z; ) can be written as:…”

Section: Persymmetric Glrt (P-glrt)mentioning

confidence: 99%

“…The covariance matrix estimator (18) is called forwardbackward sample covariance matrix, which is the ML estimator of the persymmetric covariance matrix [32]. An exact theory indicating the performance of the estimator as a function of number of independent vector z(m), m = 1, 2, .…”

Section: Persymmetric Glrt (P-glrt)mentioning

confidence: 99%

“…Adding (32) and (33) gives us the Kronecker product-based forwardbackward log-likelihood log f BF z (Z; A ⊗ T ), required for estimating the persymmetric estimate of T . Following similar steps as in (32) and (33), we can find forward-http://asp.eurasipjournals.com/content/2014/1/160 and backward-looking log-likelihood functions for finding the persymmetric estimate of A . To find the ML estimates of the covariance matrices, in the first step, we fix A and find T that maximizes log f BF z (Z; A ⊗ T ).…”

Section: Pk-glrtmentioning

confidence: 99%

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Multi-antenna spectrum sensing by exploiting spatio-temporal correlation

Ali

Ramírez

Jansson

et al. 2014

EURASIP J. Adv. Signal Process.

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In this paper, we propose a novel mechanism for spectrum sensing that leads us to exploit the spatio-temporal correlation present in the received signal at a multi-antenna receiver. For the proposed mechanism, we formulate the spectrum sensing scheme by adopting the generalized likelihood ratio test (GLRT). However, the GLRT degenerates in the case of limited sample support. To circumvent this problem, several extensions are proposed that bring robustness to the GLRT in the case of high dimensionality and small sample size. In order to achieve these sample-efficient detection schemes, we modify the GLRT-based detector by exploiting the covariance structure and factoring the large spatio-temporal covariance matrix into spatial and temporal covariance matrices. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages over existing detectors.

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“…In the following,R y is estimated by the forward-backward method to avoid sensitivity to phase errors (see [9] for a more detailed discussion on the benefits of this estimator as compared to the forward-only estimator).…”

Section: Robust Capon Spectral Estimationmentioning

confidence: 99%

Detection of cell-cyclic elements in mis-sampled gene expression data using a robust Capon estimator

Bowles

Jakobsson

Chambers

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing

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This item was submitted to Loughborough's Institutional Repository (https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions.For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/ DETECTION OF CELL-CYCLIC ELEMENTS IN MIS-SAMPLED GENE EXPRESSION DATA USING A ROBUST CAPON ESTIMATOR Thomas Bowles, Andreas Jakobsson and Jonathon ChambersCentre for Digital Signal Processing Research King's College London Strand, London, WC2R 2LS, U.K. ABSTRACTWe present a method for the estimation of possible cell cyclic elements in mis-sampled microarray data. Accurate assessment of the frequency content of microarray data gives insight into genes which could be cell-cycle regulated. Cell cycle regulation is one component of the complex network of genetic regulatory processes and is especially relevant to the study of cancer. As cDNA microarray experiments involve human sampling of cell populations, slight variations in the sampling times invariably occur. Here, we propose estimating the frequency content of microarray data using the recent robust Capon estimator, and formulate a suitable uncertainty region to minimize over. The estimator is shown to yield robust estimates with real microarray data and to identify cell-cyclic genes that elude both the traditional Periodogram and the Capon spectral estimator.

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Forward-only and forward-backward sample covariances – A comparative study

Cited by 46 publications

References 6 publications

Best causal mathematical models for a nonlinear system

Best causal mathematical models for a nonlinear system

Multi-antenna spectrum sensing by exploiting spatio-temporal correlation

Detection of cell-cyclic elements in mis-sampled gene expression data using a robust Capon estimator

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