On detection of the number of signals in presence of white noise

Zhao, Lincheng; Krishnaiah, P. R.; Bai, Zijian

doi:10.1016/0047-259x(86)90017-5

Cited by 303 publications

(140 citation statements)

References 12 publications

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“…Designed in this manner, the linear filter h is known as the Hotelling observer, or pre-whitening matched filter 11,48 . Ideally one would desire an infinite amount of data in order to calculate the exact covariance matrix K. Its estimation using a limited amount of data is a classical signal processing problem, conventionally solved with help of information-theoretic dimension reduction criteria like AIC, MDL or EDC 49,50 . In our tests, the EDC2 criterion 50 was the most robust (data not shown), and we therefore used it to estimate the reduced-dimension covariance matrix, and then to invert the matrix.…”

Section: Roc-auc Maximizing Detection Filter (Dfilter)mentioning

confidence: 99%

Uniform, optimal signal processing of mapped deep-sequencing data

et al. 2013

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Section: Roc-auc Maximizing Detection Filter (Dfilter)mentioning

confidence: 99%

Uniform, optimal signal processing of mapped deep-sequencing data

et al. 2013

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“…The form of this term is similar to the likelihood term described in many papers on signal detection [14]. The difference is the presence of the quantities v i 's defined above in place of the eigenvalues of the covariance matrix of the observed data.…”

Section: Bayesian Estimation Of the Number Of Principal Componentsmentioning

confidence: 66%

Bayesian estimation of the number of principal components

Seghouane

Cichocki

2007

Signal Processing

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Recently, the technique of principal component analysis (PCA) has been expressed as the maximum likelihood solution for a generative latent variable model. A central issue in PCA is choosing the number of principal components to retain. This can be considered as a problem of model selection. In this paper, the probabilistic reformulation of PCA is used as a basis for a Bayasian approach of PCA to derive a model selection criterion for determining the true dimensionality of data. The proposed criterion is similar to the Bayesian Information Criterion, BIC, with a particular goodness of fit term and it is consistent. A simulation example that illustrates its performance for the determination of the number of principal components to be retained is presented.

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“…The proof of Lemma 1 can be done the same way as in Zhao, Krishnaiah and Bai (1986).  be the partial correlation coefficient between X 1 and X 2 ,…”

Section: Proofmentioning

confidence: 99%

Test for the Equality of Partial Correlation Coefficients for Two Populations

Bhandary

Gupta

2015

J. Mod. App. Stat. Meth.

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A likelihood ratio test for the equality of two partial correlation coefficients based on two independent multinormal samples has been derived. The large sample Z-test for the same problem has also been discussed. The power analysis of the two tests is obtained. It has been found that the approximate likelihood ratio (ALR) test showed consistently better results than Z -test in terms of power. The size of the ALR test is slightly more than the alpha level. The ALR test is recommended strongly for use in practice.

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On detection of the number of signals in presence of white noise

Cited by 303 publications

References 12 publications

Uniform, optimal signal processing of mapped deep-sequencing data

Uniform, optimal signal processing of mapped deep-sequencing data

Bayesian estimation of the number of principal components

Test for the Equality of Partial Correlation Coefficients for Two Populations

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