- Read more about High-Dimensional Sparse Bayesian Learning without Covariance Matrices
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- Read more about Semi-supervised standardized detection of periodic signals with application to exoplanet detection
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We propose a numerical methodology for detecting periodicities in unknown colored noise and for evaluating the ‘significance levels’ (p-values) of the test statistics. The procedure assumes and leverages the existence of a set of time series obtained under the null hypothesis (a null training sample, NTS) and possibly complementary side information. The test statistic is computed from a standardized periodogram, which is a pointwise division of the periodogram of the series under test to an averaged periodogram obtained from the NTS.

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- Read more about A TEST FOR CONDITIONAL CORRELATION BETWEEN RANDOM VECTORS BASED ON WEIGHTED U-STATISTICS
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This article explores U-Statistics as a tool for testing conditional correlation between two multivariate sources with respect to a potential confounder. The proposed approach is effectively an instance of weighted U-Statistics and does not impose any statistical model on the processed data, in contrast to other well-known techniques that assume Gaussianity. By avoiding determinants and inverses, the method presented displays promising robustness in small-sample regimes. Its performance is evaluated numerically through its MSE and ROC curves.

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- Read more about A TEST FOR CONDITIONAL CORRELATION BETWEEN RANDOM VECTORS BASED ON WEIGHTED U-STATISTICS
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This article explores U-Statistics as a tool for testing conditional correlation between two multivariate sources with respect to a potential confounder. The proposed approach is effectively an instance of weighted U-Statistics and does not impose any statistical model on the processed data, in contrast to other well-known techniques that assume Gaussianity. By avoiding determinants and inverses, the method presented displays promising robustness in small-sample regimes. Its performance is evaluated numerically through its MSE and ROC curves.

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- Read more about Semi-Supervised Standardized Detection of Periodic Signals with Application to Exoplanet Detection
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We propose a numerical methodology for detecting periodicities in unknown colored noise and for evaluating the ‘significance levels’ (p-values) of the test statistics. The procedure assumes and leverages the existence of a set of time series obtained under the null hypothesis (a null training sample, NTS) and possibly complementary side information. The test statistic is computed from a standardized periodogram, which is a pointwise division of the periodogram of the series under test to an averaged periodogram obtained from the NTS.

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- Read more about ON THE USE OF GEODESIC TRIANGLES BETWEEN GAUSSIAN DISTRIBUTIONS FOR CLASSIFICATION PROBLEMS
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- Read more about ON THE USE OF GEODESIC TRIANGLES BETWEEN GAUSSIAN DISTRIBUTIONS FOR CLASSIFICATION PROBLEMS
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- Read more about Cramèr-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation
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In many practical parameter estimation problems,

such as coefficient estimation of polynomial regression, the true

model is unknown and thus, a model selection step is performed

prior to estimation. The data-based model selection step affects

the subsequent estimation. In particular, the oracle Cramér-Rao

bound (CRB), which is based on knowledge of the true model, is

inappropriate for post-model-selection performance analysis and

system design outside the asymptotic region. In this paper, we

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- Read more about Identification of Edge Disconnections in Networks Based on Graph Filter Outputs
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Graphs are fundamental mathematical structures used in various fields to model statistical and physical relationships between data, signals, and processes. In some applications, such as data processing in graphs that represent physical networks, the initial network topology is known. However, disconnections of edges in the network change the topology and may affect the signals and processes over the network. In this paper, we consider the problem of edge disconnection identification in networks by using concepts from graph signal processing.

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