52 citations · 190 across the 24 of their papers we have counts for
20 papers · 1 filter
Estimation and Clustering in Popularity Adjusted Stochastic Block Model
Majid Noroozi, Ramchandra Rimal, Marianna Pensky
The paper considers the Popularity Adjusted Block model (PABM) introduced by Sengupta and Chen (2018). We argue that the main appeal of the PABM is the flexibility of the spectral…
Density Deconvolution with Small Berkson Errors
Ramchandra Rimal, Marianna Pensky
The present paper studies density deconvolution in the presence of small Berkson errors, in particular, when the variances of the errors tend to zero as the sample size grows. It i…
Clustering in statistical ill-posed linear inverse problems
Rasika Rajapakshage, Marianna Pensky
In many statistical linear inverse problems, one needs to recover classes of similar curves from their noisy images under an operator that does not have a bounded inverse. Problems…
Dynamic network models and graphon estimation
Marianna Pensky
In the present paper we consider a dynamic stochastic network model. The objective is estimation of the tensor of connection probabilities when it is generated by a Dynamic Sto…
Solution of linear ill-posed problems using random dictionaries
Pawan Gupta, Marianna Pensky
In the present paper we consider application of overcomplete dictionaries to solution of general ill-posed linear inverse problems. In the context of regression problems, there has…
Classification with many classes: challenges and pluses
Felix Abramovich, Marianna Pensky
The objective of the paper is to study accuracy of multi-class classification in high-dimensional setting, where the number of classes is also large ("large , large , small $…
Minimax theory of estimation of linear functionals of the deconvolution density with or without sparsity
Marianna Pensky
The present paper considers a problem of estimating a linear functional of an unknown deconvolution density on the basis of i.i.d. observa…
Sparse high-dimensional varying coefficient model: non-asymptotic minimax study
Olga Klopp, Marianna Pensky
The objective of the present paper is to develop a minimax theory for the varying coefficient model in a non-asymptotic setting. We consider a high-dimensional sparse varying coeff…
Multichannel Deconvolution with Long-Range Dependence: A Minimax Study
Rida Benhaddou, Rafal Kulik, Marianna Pensky +1
We consider the problem of estimating the unknown response function in the multichannel deconvolution model with long-range dependent Gaussian errors. We do not limit our considera…
Anisotropic Denoising in Functional Deconvolution Model with Dimension-free Convergence Rates
Rida Benhaddou, Marianna Pensky, Dominique Picard
In the present paper we consider the problem of estimating a periodic -dimensional function based on observations from its noisy convolution. We construct a wavelet esti…
Non-asymptotic approach to varying coefficient model
Olga Klopp, Marianna Pensky
In the present paper we consider the varying coefficient model which represents a useful tool for exploring dynamic patterns in many applications. Existing methods typically provid…
Laplace deconvolution and its application to Dynamic Contrast Enhanced imaging
Fabienne Comte, Charles-André Cuénod, Marianna Pensky +1
In the present paper we consider the problem of Laplace deconvolution with noisy discrete observations. The study is motivated by Dynamic Contrast Enhanced imaging using a bolus of…
Spatially inhomogeneous linear inverse problems with possible singularities
Marianna Pensky
The objective of the present paper is to introduce the concept of a spatially inhomogeneous linear inverse problem where the degree of ill-posedness of operator depends not onl…
Adaptive Nonparametric Empirical Bayes Estimation Via Wavelet Series: the Minimax Study
Rida Benhaddou, Marianna Pensky
In the present paper, we derive lower bounds for the risk of the nonparametric empirical Bayes estimators. In order to attain the optimal convergence rate, we propose generalizatio…
Laplace deconvolution with noisy observations
Felix Abramovich, Marianna Pensky, Yves Rozenholc
In the present paper we consider Laplace deconvolution for discrete noisy data observed on the interval whose length may increase with a sample size. Although this problem arises i…
Multichannel Boxcar Deconvolution with Growing Number of Channels
Marianna Pensky, Theofanis Sapatinas
We consider the problem of estimating the unknown response function in the multichannel deconvolution model with a boxcar-like kernel which is of particular interest in signal proc…
On convergence rates equivalency and sampling strategies in functional deconvolution models
Marianna Pensky, Theofanis Sapatinas
Using the asymptotical minimax framework, we examine convergence rates equivalency between a continuous functional deconvolution model and its real-life discrete counterpart over a…
On optimality of Bayesian testimation in the normal means problem
Felix Abramovich, Vadim Grinshtein, Marianna Pensky
We consider a problem of recovering a high-dimensional vector observed in white noise, where the unknown vector is assumed to be sparse. The objective of the paper is to de…
Functional deconvolution in a periodic setting: Uniform case
Marianna Pensky, Theofanis Sapatinas
We extend deconvolution in a periodic setting to deal with functional data. The resulting functional deconvolution model can be viewed as a generalization of a multitude of inverse…
Frequentist optimality of Bayesian wavelet shrinkage rules for Gaussian and non-Gaussian noise
Marianna Pensky
The present paper investigates theoretical performance of various Bayesian wavelet shrinkage rules in a nonparametric regression model with i.i.d. errors which are not necessarily…