Marianna Pensky

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The paper considers regression problems with uni-variate design points. The design points are irregular and no assumptions on their distribution are imposed. The regression function is retrieved by a wavelet based reproducing kernel Hilbert space (RKHS) technique with the penalty equal to the sum of blockwise RKHS norms. In order to simplify numerical(More)
The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression profiles. Each gene expression profile is modeled as an expansion(More)
The problem of estimating a density g based on a sample X 1 X 2 X n from p = q * g is considered. Linear and nonlinear wavelet estima-tors based on Meyer-type wavelets are constructed. The estimators are asymptotically optimal and adaptive if g belongs to the Sobolev space H α. Moreover, the estimators considered in this paper adjust automatically to the(More)
BACKGROUND Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular(More)
Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel and(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t A truly functional Bayesian method for detecting temporally(More)
We study the simultaneous detection of multiple structures in the presence of overwhelming number of outliers in a large population of points. Our approach reduces the problem to sampling an extremely sparse subset of the original population of data in one grab, followed by an unsu-pervised clustering of the population based on a set of instantiated models(More)
The problem of estimating the log-spectrum of a stationary Gaussian time series by Bayesianly induced shrinkage of empirical wavelet coefficients is studied. A model in the wavelet domain that accounts for distributional properties of the log-periodogram at levels of fine detail and approximate normality at coarse levels in the wavelet decomposition, is(More)
Evolutionary studies of communication can benefit from classification procedures that allow individual animals to be assigned to groups (e.g. species) on the basis of high-dimension data representing their signals. Prior to classification, signals are usually transformed by a signal processing procedure into structural features. Applications of these signal(More)
In the present paper we consider a dynamic stochastic network model. The objective is estimation of the tensor of connection probabilities Λ when it is generated by a Dynamic Stochastic Block Model (DSBM) or a dynamic graphon. In particular, in the context of DSBM, we derive penalized least squares estimator Λ of Λ and show that Λ satisfies an oracle(More)