Recent Publications

Books and Book Chapters:

  • Kotz, S., Lumelskii, Y., Pensky, M. (2003)
    The Stress-Strength Model and Its Generalizations. Theory and Applications.
    World Scientific Co., Singapore, 253 pp.  Google Books
  • Pensky, M. (2006)
    Frequentist optimality of Bayesian wavelet shrinlkage rules.
    In “Splines and Wavelets: Athens 2005“. G. Chen and M.-J. Lai, eds.,
    Nashboro Press, Brentwood, TN, 390–401.
  • Pensky, M. (2007)
    Empirical Bayes estimation of reliability.
    In “Encyclopedia of Statistics in Quality and Reliability“,
    F. Ruggery, R.  Kenett, F.W.  Faltin (eds)., Wiley, Chichester, UK,
    559–606.   PDF
  • Angelini, C.,   De Canditiis, D.,   Pensky, M. (2012)
    Bayesian methods for time course microarray analysis: from genes’ detection to clustering.
    In  Advanced Statistical Methods for the Analysis of Large Data-Sets,
    Di Ciaccio, A., Coli, M., Angulo Ibanez, J. M. (Eds.) Springer,  pp. 47-56.
  •  Davis,  J.,  Pensky, M.  (2014)
    Model Selection for Classification with a Large Number of Classes.
    In  Topics in Nonparametric Statistics. Springer Proceedings in Mathematics & Statistics, 74,
    Akritas, M.G., Lahiri, S.N., Politis, D.N.,  Eds., pp. 251–258.

Research Papers:


  • Bhattacharyya,B.B, Li, X., Pensky, M., and Richardson, G.D. (2000)
    Testing for unit roots in nearly nonstationary spatial autoregressive process.
    Annals of the Institute of Statistical Mathematics,   52, 71 — 83.
  • Pensky,M., and  Ni, P. (2000)
    Extended linear empirical Bayes estimation.
    Communications in Statistics – Theory and Methods,  29, 579 — 592.
  • Pensky, M., and  Kirtane, K. (2000)
    Linear empirical Bayes estimation in the case of the Wishart distribution.
    Communications in Statistics. Theory and Methods,  29, 1787–1799.
  • Pensky, M. (2000)
    Adaptive wavelet empirical Bayes estimation of a location or a scale parameter.
    Journal of Statistical Planning and Inference,  90, 275 –292.
  • Elhor,A., and  Pensky, M. (2000)
    Bayesian  estimators of locations of lightning events.
    Sankhya,  B62, 202 — 216.


  • Pensky, M., and  Vidakovic, B. (2001)
    On non-equally spaced wavelet regression.
    Annals of the Institute of Statistical Mathematics,  53, 681–690.


  • Pensky, M.  (2002)
    Locally adaptive wavelet empirical Bayes estimation of a location  parameter,
    Annals of the Institute of Statistical Mathematics,  54, 83–99.
  • Pensky, M. (2002)
    Density deconvolution based on wavelets with bounded supports.
    Statistics and Probability Letters,  56, 261–269.
  • Singh, R.S.,  and  Pensky, M.  (2002)
    Non-parametric estimation of prior densities of multidimensional location and scale parameters with rates and best possible.
    The  Journal of Mathematical Sciences. New Series, 1, 86–105.
  • Pensky, M., and Zayed, A.I. (2002)
    Density deconvolution of different conditional distributions.
    Annals of the Institute of Statistical Mathematics,  54, 701–712.
  • Pensky, M. (2002)
    A new approach to empirical Bayes estimation with errors in variables.
    Statistics and Decisions,  20, 225–240.


  • Pensky, M. (2003)
    Rates of convergence of   empirical Bayes tests for a normal mean.
    Journal of Statistical Planning and Inference,  11, 181–196.
  • Pensky, M. (2003)
    Estimation of probabilities of linear inequalities for  independent elliptic  random vectors.
    Sankhya,  65, 91–106.   PDF


  • De Canditiis, D., Pensky, M. (2004)
    Discussion on the meeting on “Statistical approaches to inverse problems”.
    Journ. Roy. Statist. Soc., Ser. B,  66, 638–640.


  • Pensky, M., Allotaibi, M.  (2005)
    Generalization of linear empirical Bayes estimation via wavelet series.
    Statistics and Decisions,  23, 181–198.   PDF


  • Amato, U., Antoniadis, A., Pensky, M. (2006)
    Wavelet kernel penalized estimation for non-equispaced design regression.
    Statistics and Computing,  16, 37–55.   PDF
  • De Canditiis, D., Pensky, M. (2006)
    Simultaneous Wavelet Deconvolution in Periodic Setting.
    Scandinavian Journal of Statistics,  33, 293–306.   PDF
  •  Pensky, M. (2006)
    Frequentist optimality of Bayesian wavelet shrinlkage rules for Gaussian and non-Gaussian noise.
    Annals of Statistics,  34,  769–807.   PDF
  • Heard, A., Pensky, M. (2006)
    Confidence intervals for reliabilty and quantile function with application to NASA  Space Flight data.
    IEEE Transactions in Reliability,  55, 591–601.   PDF


  • Pensky, M.,   Vidakovic, B. and De Canditiis, D. (2007)
    Bayesian decision theoretic scale-adaptive estimation of log-spectral density.
    Statistica Sinica,  17, 635–666.   PDF
  • Pensky, M., Sapatinas, T.  (2007)
    Frequentist optimality of Bayes Factor estimators in wavelet regression models.
    Statistica Sinica,  17, 599–633.   PDF
  • Angelini, C.,   De Canditiis, D., Mutarelli, M., Pensky, M. (2007)
    Bayesian approach to estimation and testing in time course microarray experiments.
    Statistical Applications in Genetics and Molecular Biology, 6, \#1, Article 24, 1–30.   PDF
  • Abramovich, F.,  Grinshtein, V., Pensky, M.   (2007)
    On optimality of Bayesian testimation in the normal means problem.
    Annals of Statistics,  35,  2261–2286.   PDF
  • Abramovich, F.,   Antoniadis, A.,  Pensky, M. (2007)
    Estimation of piecewise-smooth functions by amalgamated bridge regression splines.
    Sankhya,  70, 1–27.   PDF


  • Bradshaw, D.J.,  Pensky, M. (2008)
    Decision theory based classification of high-dimensional vectors based on small samples.
    Test, 17, 83—100.   PDF
  • Brownstein, N.,   Pensky, M. (2008)
    Application of transformations in parametric inference.
    Journal of Statistics Education, 16, No. 1, 1–10.  PDF
  • Angelini, C., Cutillo, L.,   De Canditiis, D., Mutarelli, M., Pensky, M. (2008)
    BATS: a Bayesian user-friendly software for Analyzing Time Series microarray experiments.
    BMC: Bioinformatics,  9, No. 415.
    DOI:    PDF
  • Crampton, W.G.R., Davis,  J.K.,  Lovejoy,  N.R.,  Pensky, M. (2008)
    Multivariate classification of animal communication signals: a simulation-based comparison of alternative signal processing procedures, using electric fishes.
    Journal of Physiology – Paris, 102, 304–321.   PDF


  • Pensky, M., Sapatinas, T. (2009)
    Functional Deconvolution in a Periodic Setting: Uniform Case.
    Annals of Statistics, 37, 73–104. .     PDF
  • Angelini, C.,   De Canditiis, D.,   Pensky, M. (2009)
    Bayesian models for the two-sample time-course microarray experiments.
    Computational Statistics & Data Analysis,  53, 1547–1565.  .   PDF


  • Bradshaw, D.J.,  Pensky, M. (2010)
    SVM-like decision theoretical classification of high-dimensional vectors.
    Journ. Stat. Plan. Inf., 140, 705 — 718.       PDF
  • Pensky, M., Sapatinas, T.  (2010)
    On Convergence Rates Equivalency and Sampling Strategies in a Functional Deconvolution Model.
    Annals of Statistics, 38, 1793–1844.       PDF
  • Song, D.,   Fedorenko, I.,   Pensky, M.,   Qian, W.,  Tockman, M.,  and  Zhukov, T.  (2010)
    Quantificational and Statistical Analysis of the Differences in Centrosomal Features of Untreated Lung Cancer Cells and Normal Cells.
    Analytical and Quantitative Cytology and Histology, 32, No. 5, article  280   PDF


  • Angelini, C.,   De Canditiis, D.,   Pensky, M. (2011)
    Estimation and Testing in Time-course MicroarrayExperiments.  
    Bayesian Modeling in Bioinformatics.
    Eds. Dey, D.K.,  Ghosh, S., and  Mallick,B.K.,Chapman and Hall/CRC, Boca Raton, pp. 1–26.   PDF
  • Pensky, M., Sapatinas, T. (2011)
    Multichannel Boxcar Deconvolution with Growing Number of Channels. 
    Electronic Journal of Statistics,   5, 53–82   PDF
  • Davis, J.,  Pensky, M.,  and Crampton, W. (2011)
    Bayesian Feature Selection for Classification with Possibly Large Number of Classes.
    Journal of Statistical Planning and Inference, 141,   3256–-3266     PDF
  • Huo, Q.,  Cordero, A.,    Bogdanovic, J.,    Colon, J.,  Baker, C.H.,   Goodison, S.,  Pensky, M. (2011)
    A Facile Nanoparticle Immunoassay to Detect Multiple Biomarkers in Serum Samples.
    Journal of Nanobiotechnology9 (20)


  • Angelini, C.,   De Canditiis, D.,   Pensky, M. (2012)
    Clustering  Time-Course Microarray Data Using Functional Bayesian Infinite Mixture Model.
    Journal of Applied Statistics,  39, 129-149.   PDF


  • Klopp, O.,  Pensky, M. (2013)
    Non-asymptotic approach to varying coefficient model.
    Electronic Journal of Statistics,  7,  454-479.   PDF
  • Abramovich, F., Pensky, M.,  Rozenholc, Y. (2013)
    Laplace deconvolution with noisy observations.
    Electronic Journal of Statistics,  7,  1094–1128.   PDF
  • Benhaddou, R., Pensky, M., Picard, D. (2013)
    Anisotropic Denoising in Functional Deconvolution Model with Dimension-free Convergence Rates.
    Electronic Journal of Statistics,  7,  1686–1715.   PDF
  • Benhaddou, R., Pensky, M.  (2013)
    Adaptive  Nonparametric Empirical Bayes Estimation Via Wavelet Series.
    Journ. Stat. Plan. Inference,  143,  1672–1688.   PDF
  • Pensky, M. (2013)
    Spatially inhomogeneous linear inverse problems with possible singularities.
    Annals of Statistics, 41, 2668–2697.   PDF   SOFTWARE


  • Antoniadis, A., Pensky, M., Sapatinas, T. (2014)
    Nonparametric Regression Estimation with Incomplete Data: Minimax Global Convergence Rates   and Adaptivity.
    ESAIM, 18, 1-41.   PDF
  • Benhaddou, R., Kulik, R., Pensky, M., Sapatinas, T. (2014)
    Multichannel Deconvolution with Long-Range Dependence: A Minimax Study.    
    Journ. Stat. Plan. Inference,  148, 1-19 (invited paper).   PDF
  • Davis, J., Pensky, M.  (2014)
    Model Selection for Classification with a Large Number of Classes.
    In: Topics in Nonparametric Statistics. Springer Proceedings in Mathematics & Statistics, 74,
    Akritas,M.G., Lahiri, S.N., Politis, D.N., Eds., 251–258   PDF


  • Klopp,   O., Pensky, M. (2015)
    Sparse high-dimensional varying coefficient model: non-asymptotic minimax study.  
    Ann. Stat, 43, 1273–1299.   PDF
  • Liu, B., Wang, O., Tappen, M., Foroosh, H., Pensky, M. (2015)
    Sparse convolutional neural networks.
    CVPR Proceedings 2015,   806–814.   PDF
  • Jaberi, M.,   Pensky, M., Foroosh, H. (2015)
    Sparse  Withdrawal of Inliers in a First Trial (SWIFT). 
    CVPR Proceedings 2015,  4849–4857.    PDF


  • Pensky, M.
    Solution of linear ill-posed problems using overcomplete dictionaries.  
    Ann. Statist., 44, 1739-1764.  PDF
  • De Canditiis, D.,   Pensky, M.
    Estimation of delta-contaminated density of the random intensity   of Poisson data.
    Electronic Journal of Statistics, 10,  683-705.    PDF
  • Pensky, M.
    Minimax theory of estimation of linear functionals of the deconvolution density
    with or without sparsity.  Ann. Statist., accepted  PDF
  • Pensky, M.
    Dynamic network models and graphon estimationArXiv1607.00673
  • Gupta, P., Pensky, M.
    Solution of linear ill-posed problems using random dictionariesArXiv1605.07913      MatLabFiles


  • Comte,  F., Cuenod,  C.-A., Pensky, M.,   Rozenholc, Y.  (2017)
    Laplace deconvolution on the basis of   time domain data  and its application to Dynamic Contrast Enhanced imaging. 
    Journal of the Royal Stat. Society, Ser. B,  79,   69–94   PDF
    SOFTWARE (courtesy Yves Rozenholc)
  • Abramovich, F., Pensky, M.   Feature selection and classification of high-dimensional normal vectors with possibly large number of classes.   PDF
  • Benhaddou, R., Pensky, M.,  Rajapakshage, R.  Anisotropic functional Laplace decnvolution  PDF
  • Pensky, M., Zhang, T.   Spectral clustering in the dynamic stochastic block model.   PDF