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 shrinkage 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:

2000

• 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.

2001

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

2002

• 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.

2003

• 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

2004

• 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.

2005

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

2006

• 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

2007

• 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

2008

• 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.
  http://www.amstat.org/publications/jse/jse_archive.htm#2008  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: http://www.biomedcentral.com/1471-2105/9/415    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

2009

• 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

2010

• 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
  

2011

• 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 Nanobiotechnology,  9 (20)
  DOI: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3127990/pdf/1477-3155-9-20.pdf

2012

• 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

 2013

• 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

2014

• 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

2015

• 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

2016

• 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

2017

• 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)
• Pensky, M.
  Minimax theory of estimation of linear functionals of the deconvolution density
  with or without sparsity.  Ann. Statist., 45,  1516–1541  PDF
  
  

• 2018

• Abramovich, F., De Canditiis, D., Pensky, M. (2018)  Solution of linear ill-posed problems by model selection and aggregation.  12, 1822-1841. PDF

• D. De Canditiis, M. Pensky, P.J. Wolfe (2018)
  Denoising strategies for general finite frames. Mathematics and Computers in Simulation, 147,  90–99   PDF
• Gupta, P., Pensky, M.
  Solution of linear ill-posed problems using random dictionaries. Sankhya, Ser. B,   PDF    MatLabFiles
• Jaberi, M., Pensky, M., Foroosh, H. (2018) Probabilistic Sparse Subspace Clustering Using Delayed Association. Proceedings of the 2018 24th International Conference
  on Pattern Recognition (ICPR), 2087-2092.  PDF

• 2019

• Abramovich, F., Pensky, M.   (2019)  Classification with many classes: Challenges and pluses.  Journal of Multivariate Analysis, 174,  #104536.  PDF
• Pensky, M., Zhang, T. (2019) Spectral clustering in the dynamic stochastic block model. Electronic Journal of Statistics, 13, 678-709   PDF
• Pensky, M. (2019)  Dynamic network models and graphon estimation. Ann. Statist., 47, 2378–2403  PDF
• Benhaddou, R., Pensky, M.,  Rajapakshage, R. (2019)  Anisotropic functional Laplace decnvolution  Journ. Statist. Plan. Inf., 199, 271–285.  PDF
• Rimal, R., Pensky, M. (2019) Density Deconvolution with Small Berkson Errors. Mathematical Methods of Statistics, 28, 208–227.  PDF
• Jaberi, M., Pensky, M., Foroosh, H. (2019)  SWIFT: Sparse Withdrawal of Inliers in a First Trial. IEEE Transactions on Pattern Analysis and Machine Intelligence,  41, 3057 – 3070.   PDF

• 2020

• Rajapakshage, R., Pensky, M.  (2020)   Clustering in statistical ill-posed linear inverse problems. IEEE Transactions on Information Theory,  66, 7180-7195  PDF

• 2021

• Noroozi, M., Rimal, R., Pensky, M. (2021) Estimation and Clustering in Popularity Adjusted Stochastic Block Model.  Journal of Royal Stat. Soc., Ser. B, 83, 293-317.  PDF     MatLab Codes
• Noroozi, M., Pensky, M. (2021)  Statistical Inference in Heterogeneous Block Model. The Hierarchy of Block Models.  Sankhya, Ser.A., 84, 64-107
  ArXiv: 2002.02610     MatLab Codes
• Noroozi, M., Rimal, R., Pensky, M. (2021)  Sparse Popularity Adjusted Stochastic Block Model. Journ. Machine Learn. Res., 22, 1-36    PDF   MatLab Codes
• Pensky, M. , Wang, Y. (2021)  Clustering of Diverse Multiplex Networks. See Year 2024 for MatLab Code and Data Sets.  ArXiv:2110.05308

      2022

• Fan, X., Pensky, M., Yu, F., Zhang, T. (2022)  ALMA: Alternating Minimization Algorithm For Clustering Mixture Multilayer Network.  Journal of Machine Learning Research, 23, #330, 1-46.  PDF

• Pensky, M. (2022) Invited Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis” by Nikolaos Ignatiadis and Stefan Wager.  Journal of the  American Statistical Association, 117, 1183–1185.  PDF
• Pensky, M. (2022)  Invited Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Karl Rohe and Muzhe Zeng.  Journal of the Royal Statistical Society, Ser. B,  85, 1062-1066. PDF

  2024

• Pensky, M. , Wang, Y. (2024)  Clustering of Diverse Multiplex Networks. IEEE Transactions on Network Science and Engineering, accepted.   MatLab Code and Data Sets 
• Pensky, M. , Noroozi, M. (2024)   Sparse Subspace Clustering in Diverse Multiplex Network Model.       MatLab Code and Data Sets 
• Pensky, M. (2024)  Signed Diverse Multiplex Networks: Clustering and Inference. ArXiv:2402.10242