* indicates student co-author
- Mukhopadhyay S. (2021) A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications. This paper is written for the special occasion of E. T. Jaynes’s (1922–1998) birth centenary celebration.
- Mukhopadhyay S. (2021) InfoGram and Admissible Machine Learning.
- Mukhopadhyay, S., and Wang, K* (2020) Breiman’s “Two Cultures” Revisited and Reconciled. [PDF] It celebrates the 70th anniversary of (Nonparametric) Statistical Machine Learning.
- Mukhopadhyay, S., and Wang, K* (2020) [PDF] On The Problem of Relevance in Statistical Inference. It addresses “The Relevance Problem”, a 50 years old statistical inference puzzle.
- Mukhopadhyay, S., and Wang, K* (2020) Spectral Graph Analysis: A Unified Explanation and Modern Perspectives. [Slide] We dedicate this research to the beloved memory of Emanuel “Manny” Parzen (1929–2016) on the occasion of his 90th birthday anniversary.
Five Selected Publications
- 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.
- 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 and in recognition of his pioneering work Friedman & Rafsky(1979), which inspired this research.
- 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.
- 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.
- 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]
- Mukhopadhyay, S. (2020) United Statistical Algorithms and Data Science: An Introduction to the Principles. [Online] Springer Mathematics & Statistics, vol 339, p 367-377.
- 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.
- Mukhopadhyay, S. (2018) Decentralized Nonparametric Multiple Testing. [PDF], Journal of Nonparametric Statistics, 30, 1003-1015.
- 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]
- Mukhopadhyay, S. (2017) Statistics Educational Challenge in the 21st Century, Biostatistics and Biometrics Journal, Invited Opinion Article. [PDF]
- 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]
- Mukhopadhyay, S. (2015) Invited Review of “Analysis of Multivariate and High-Dimensional Data,” Journal of the American Statistical Association, 110, 1320.
- Parzen, E. and Mukhopadhyay, S. (2013) United Statistical Algorithms, LP comoment, Copula Density, Nonparametric Modeling. 59th ISI World Statistics Congress (WSC), Hong Kong. [PDF]
- 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 ]
- 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 ]
- 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.
- 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 ]
- 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 ]
- 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 ]
- 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. [ PDF]