Publications

* indicates Temple student author

  1. Mukhopadhyay, S. and Fletcher,  D*. (2018)  Generalized Empirical Bayes Modeling via Frequentist Goodness-of-Fit. [PDF, SlidesNewsNature Scientific Reports, 8 (9983), 1-15.   “I asked myself could one be an empirical Bayesian. Perhaps starting with a strongly or weakly held initial prior but then modifying it to reflect the accumulation of evidence provided by not the unobservable theta-i’s but the observable Xi’s” — Herbert Robbins on The origins of Empirical Bayes.
  2. Mukhopadhyay, S. (2018)  Decentralized Nonparametric Multiple Testing. [PDF], Journal of Nonparametric Statistics30, 1003-1015.
  3. 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.
  4. Bruce, S.*, Li, Z*., Yang, H*., and Mukhopadhyay, S. (2018) Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications. IEEE Transactions on Big Data (forthcoming). Best Paper Award, JSM ASA Section on Nonparametric Statistics. Winner Fox School Ph.D. Student Research Competition and Fox Young Scholar Grant.  [PDF].
  5. Mukhopadhyay, S. (2017) Statistics Educational Challenge in the 21st Century, Biostatistics and Biometrics Journal, Invited Opinion Article. [PDF]
  6. 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]
  7. 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]
  8. 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]
  9. Mukhopadhyay, S. (2015) Invited Review of “Analysis of Multivariate and High-Dimensional Data,” Journal of the American Statistical Association, 110, 1320.
  10. Parzen, E. and Mukhopadhyay, S. (2013) United Statistical Algorithms, LP comoment, Copula Density, Nonparametric Modeling. 59th ISI World Statistics Congress (WSC), Hong Kong. [PDF]
  11. 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 ]
  12. 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 ]
  13. 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.
  14. 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 ]
  15. 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 ]
  16. 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 ]
  17. 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]

Under review

  1. Mukhopadhyay, S. , and Wang, K* (2019)  Spectral Graph Analysis: A Unified Explanation and Modern Perspectives. [PDF] [ISNPS][Slides][ISI]
  2. Mukhopadhyay, S., and Wang, K* (2019) A Nonparametric Approach to High-dimensional K-sample Comparison Problem. [PDF Winner 2017 Fox School Ph.D. Student Research Competition.

Preprints

  1. Mukhopadhyay, S. The NP-Problem in Big Data Discovery. One full day short course will be offered on this topic at the FTC 2017, to be held in Philadelphia (October 4, 2017, 8:30 am – 5:30 pm) http://www.falltechnicalconference.org/short-courses/
  2. Mukhopadhyay, S. and Parzen, E. LP Approach to Statistical Modeling. arXiv:1405.2601. [PDF]
  3. Parzen, E. and Mukhopadhyay, S. LP Mixed Data Science: Outline of Theory. arXiv:1311.0562.  [PDF]
  4. Parzen, E. and Mukhopadhyay, S. United Statistical Algorithm, Small and Big Data: Future of Statistician, arXiv:1308.0641. [PDF]
  5. Parzen, E. and Mukhopadhyay, S. (2012)  Modeling, Dependence, Classification, United Statistical Science, Many Cultures [PDF]