Deep Mukhopadhyay, Ph.D.

* indicates student co-author

Under Review

  1. Mukhopadhyay S. (2023) Statistical Model Development: What Happens When a Model Encounters New Data?
  2. Mukhopadhyay, S., and Wang, K* (2023) Conditional Density Sharpening: Theory, Concepts, and Tools
  3. Mukhopadhyay S. (2022) Density Sharpening: Principles and Applications to Discrete Data Analysis. [PDF]
  4. Mukhopadhyay, S., and Wang, K* (2021) Breiman’s “Two Cultures” Revisited and Reconciled. [PDF] It celebrates the 70th anniversary of (Nonparametric) Statistical Machine Learning.

Ten Selected Publications

  1. Mukhopadhyay S. (2023) Modelplasticity and Abductive Decision Making [Online, PDF, Suppl]  Decisions in Economics and Finance, 46, 255-276.
  2. Mukhopadhyay, S., and Wang, K* (2023) [PDF] On The Problem of Relevance in Statistical Inference. Econometrics and Statistics, special issue Biostatistics, 25 (4), 93-109. It addresses “The Relevance Problem”, a 50 years old statistical inference puzzle.
  3. Mukhopadhyay S. (2022) Abductive Inference and C. S. Peirce: 150 Years Later. [PDF , Online], Journal of Quantitative Economics, 21, 123–149.
  4. Mukhopadhyay S. (2022) InfoGram and Admissible Machine Learning [PDF , Talk], Machine Learning, Special Issue on “Foundations of Data Science,”  111 (1), 205-242. Dedicated to the memory of Leland Wilkinson — a wonderful person and a strong supporter of this research.
  5. Mukhopadhyay S. (2022) A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications. [PDF], Journal of Nonparametric Statistics, 34 (4), 1036-1062. written for the occasion of E. T. Jaynes’s (1922—1998) birth centenary celebration.
  6. Mukhopadhyay, S. and Parzen, E. (2020) Nonparametric Universal Copula Modeling, Applied Stochastic Models in Business and Industry, special issue on “Data Science”, 36(1), 77-94 [Online] This paper celebrates 60th anniversary of copula.
  7. Mukhopadhyay, S., and Wang, K* (2020)  Spectral Graph Analysis: A Unified Explanation and Modern Perspectives. Nature: Scientific Reports [Slide] We dedicate this research to the beloved memory of Emanuel “Manny” Parzen (1929–2016) on the occasion of his 90th birthday anniversary.
  8. Mukhopadhyay, S., and Wang, K* (2019) A Nonparametric Approach to High-dimensional K-sample Comparison Problems. [Online, Supp] Biometrika 107 (3), 555–572.  Dedicated to Jerry Friedman on the occasion of his 80th birthday.
  9. Mukhopadhyay, S. and Fletcher,  D*. (2018)  Generalized Empirical Bayes Modeling via Frequentist Goodness-of-Fit. [PDF, Link, Slides, News] Nature: Scientific Reports, 8 (9983), 1-15.  Dedicated to Brad Efron’s 80th birthday anniversary, in appreciation of his preeminent role in the development of Empirical Bayes.
  10. Mukhopadhyay, S. (2017) Large-Scale Mode Identification and Data-Driven Sciences. Electronic Journal of Statistics, 11 215–240. Dedicated to the Legacy of “Parzen window” that remained relevant for more than 50 years. [PDF] [code]

Other Publications

  1. Mukhopadhyay, S. (2020) United Statistical Algorithms and Data Science: An Introduction to the Principles. [Online] Springer Mathematics & Statistics, vol 339, p 367-377.
  2. Bruce, S.*, Li, Z*., Yang, H*., and Mukhopadhyay, S. (2019) Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications. IEEE Transactions on Big Data, 5(2), 166-179.  [PDF] Best Paper Award, JSM ASA Section on Nonparametric Statistics.
  3. Mukhopadhyay, S. (2018)  Decentralized Nonparametric Multiple Testing. [PDF], Journal of Nonparametric Statistics, 30, 1003-1015.
  4. Mukhopadhyay, S. and Parzen, E. (2018) Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application. [Online]  J. Risk and Financial Management, Special Issue “Applied Econometrics”, 11(3),  37, 1-17.
  5. Mukhopadhyay, S. and Nandi, S* (2017) LPiTrack: Eye Movement Pattern Recognition Algorithm and Application to Biometric Identification. Machine Learning Journal, 107(2), 313-331. [PDF]. Best Paper Award, JSM ASA Section on Statistical Computing. Winner (among 82 competing algorithms) of the IEEE International Biometric Eye Movements Verification and Identification Competition. Winner 2016 Fox School Ph.D. Student Research Competition. [Slides]
  6. Mukhopadhyay, S. (2017) Statistics Educational Challenge in the 21st Century, Biostatistics and Biometrics Journal, Invited Opinion Article. [PDF]
  7. Mukhopadhyay, S. (2016) Large-Scale Signal Detection: A Unifying View. Biometrics, 72 325–334. Dedicated to John Tukey, the pioneer of multiple comparison idea, on the occasion of his 100th birthday. [PDF] [code]
  8. Mukhopadhyay, S. (2015) Invited Review of “Analysis of Multivariate and High-Dimensional Data,” Journal of the American Statistical Association, 110, 1320.
  9. Parzen, E. and Mukhopadhyay, S. (2013) United Statistical Algorithms, LP comoment, Copula Density, Nonparametric Modeling. 59th ISI World Statistics Congress (WSC), Hong Kong. [PDF]
  10. Parzen, E. and Mukhopadhyay, S. (2012) Invited discussion of “Probabilistic Index Models” by Olivier Thas et al, Journal of Royal Statistical Society, Series B, 74. [ PDF ]
  11. Lahiri, S. N. and Mukhopadhyay, S. (2012) On the Mahalanobis-distance based Penalized Empirical Likelihood Method in High Dimensions. Statistics and Its Interface, 5, 331–338. Empirical Likelihood Method in High Dimensions. Statistics and Its Interface, 5, 331-338. [ PDF ]
  12. Lahiri, S. N., and Mukhopadhyay, S. (2012). A Penalized Empirical Likelihood Method in High Dimensions. Annals of Statistics, 40 2511–2540. [ PDF ] Best Paper Award, JSM ASA Section on Nonparametric Statistics.
  13. Lahiri, S.N. and Mukhopadhyay, S. (2011) Invited discussion of “Subsampling weakly dependent time series and application to extremes” by Doukhan, P., Prohl, S. and Robert, C. TEST, 20 491-496. [ PDF ]
  14. Mukhopadhyay, S., Parzen, E. and Lahiri, S.N. (2011) From Data to Constraints. Bayesian Inference And Maximum Entropy Methods In Science And Engineering: 31st International Workshop, Waterloo. [ PDF ]
  15. Mukhopadhyay, S. and Ghosh, A.K. (2011) Ensemble Methods for Supervised and Semi-supervised Classification Using Kernel Density Estimates. Computational Statistics \& Data Analysis, 55 2344-2353. [ PDF ]
  16. Mukhopadhyay, S. and Liang, F. (2009) Bayesian Analysis of High Dimensional Classification. Bayesian Inference And Maximum Entropy Methods In Science And Engineering: 29th International Workshop, Oxford 1193, 243-250. [ PD