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pro vyhledávání: '"Robinson, Benjamin D."'
This paper considers the quickest search problem to identify anomalies among large numbers of data streams. These streams can model, for example, disjoint regions monitored by a mobile robot. A particular challenge is a version of the problem in whic
Externí odkaz:
http://arxiv.org/abs/2303.09647
Autor:
Latimer, Van, Robinson, Benjamin D.
Ledoit and Peche proved convergence of certain functions of a random covariance matrix's resolvent; we refer to this as the Ledoit-Peche law. One important application of their result is shrinkage covariance estimation with respect to so-called Minim
Externí odkaz:
http://arxiv.org/abs/2302.13708
Autor:
Robinson, Benjamin D., Malinas, Robert, Latimer, Van, Morrison, Beth Bjorkman, Hero, Alfred O.
Hotelling's $T^2$ test is a classical approach for discriminating the means of two multivariate normal samples that share a population covariance matrix. Hotelling's test is not ideal for high-dimensional samples because the eigenvalues of the estima
Externí odkaz:
http://arxiv.org/abs/2202.12725
In this paper, we consider the problem of determining the presence of a given signal in a high-dimensional observation with unknown covariance matrix by using an adaptive matched filter. Traditionally such filters are formed from the sample covarianc
Externí odkaz:
http://arxiv.org/abs/2103.11830
The Fisher-Rao geodesic distance on the statistical manifold consisting of zero-mean p-dimensional multivariate Gaussians appears without proof in several places (such as Steven Smith's "Covariance, Subspace, and Intrinsic Cramer-Rao Bounds"). In thi
Externí odkaz:
http://arxiv.org/abs/2010.15861
Publikováno v:
IEEE Transactions on Signal Processing (2021)
An important problem in space-time adaptive detection is the estimation of the large p-by-p interference covariance matrix from training signals. When the number of training signals n is greater than 2p, existing estimators are generally considered t
Externí odkaz:
http://arxiv.org/abs/2010.03388
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