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A topological multiple testing approach to peak detection is proposed for the problem of detecting transcription factor binding sites in ChIP-Seq data. After kernel smoothing of the tag counts over th...
For multivariate continuous data, the contaminated Gaussian distribution - having two parameters indicating the proportion of outliers and the degree of contamination - represents a convenient and nat...
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...
This paper introduces a spatio-temporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatio-temporal data. The model is derived as a sp...
Given the output of a data source taking values in a finite alphabet, we wish to detect change-points, that is times when the statistical properties of the source change.
We study a problem of distributed detection of an event in a large extent wireless sensor network (WSN), where the event influences the observations of the sensors only in the vicinity of where it oc...
We propose a novel probabilistic method for detection of objects in noisy images. The method uses results from percolation and random graph theories. We present an algorithm that allows to detect obje...
Single fault sequential change point problems have become important in modeling for various phenomena in large distributed systems, such as sensor networks. But such systems in many situations present...
Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection...
We consider the detection of multivariate spatial clusters in the Bernoulli model with N locations, where the design distribution has weakly dependent marginals. The locations are scanned with a rec...
Adaptive sampling results in dramatic improvements in the re- covery of sparse signals in white Gaussian noise. A sequential adap- tive sampling-and-refinement procedure called distilled sensing (DS...
Adaptive sampling results in dramatic improvements in the re- covery of sparse signals in white Gaussian noise. A sequential adap- tive sampling-and-refinement procedure called distilled sensing (DS...
A method of ‘network filtering’ has been proposed recently to detect the effects of certain external perturbations on the interacting members in a network. However, with large networks, the goal of de...
Hidden regular variation defines a subfamily of distributions satisfying multivariate regular variation on E = [0,1]d\{(0, 0, · · · , 0)} and models another regular variation on the sub-cone E(2) = E...

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