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Faculty

Ujjwal  Das
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Ujjwal Das
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Operations Management, Quantitative Methods and Information Systems

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  • Postdoctoral Research Fellow, Biostatistics University of Massachusetts, Amherst, MA
  • Doctor of Philosophy , Statistics Northern Illinois University, 2011, Dekalb, IL
  • Master of Science , Statistics Iowa State University, Ames, IA
  • Master of Science , Statistics University of Calcutta, Calcutta, India
  • Bachelor of Science , Statistics Presidency College, Calcutta, India

HONORS
  • Certificate of Merit award in recognition of superior academic achievement in the Department of Mathematical Sciences, Northern Illinois University.
PUBLICATIONS
  • Das U. and Basu R. (2021). Approximate confidence intervals for the difference in proportions for partially observed binary data. Accepted for publication in Statistical Methods in Medical Research.
  • Dhar S. S., Kundu D., & Das U. (submitted 2021). Inspecting Structural Changes of Chirp Signal Model.
  • Das U. and Das K. (2021). Selection of influential variables in ordinal data with preponderance of zeros. Statistica Neerlandica, 75(1), 66-87.
  • Dhar S. S. and Das U. (2021). On distance based goodness of fit tests for missing data when missing occurs at random. Australian & New Zealand Journal of Statistics, 63(2), 331-356.
  • Dhar S. S., Kundu D., & Das U. (2019). Tests For the Parameters of Chirp Signal Model. IEEE Transactions on Signal Processing, 67(16), 4291-4301.
  • Mitra D., Das U., & Das K. (2019). Analysis of interval-censored competing risks data under missing causes. Journal of Applied Statistics, 47(3), 439-459.
  • Lahiri S.N., Das U. & Nordman D.J. (2019). Empirical Likelihood for a long range dependent process subordinated to a Gaussian process. Journal of Time Series Analysis, 40(4), 447-466.
  • Maity A.K., Pradhan V. & Das U. (2019). Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable, The American Statistician, 73(4), 340-349.
  • Das U., & Das K. (2018). Inference on zero inflated ordinal models with semiparametric link. Computational Statistics & Data Analysis, 128, 104-115.
  • Das U., Dhar S. S., & Pradhan V. (2018). Corrected likelihood-ratio tests in logistic regression using small-sample data. Communications in Statistics-Theory and Methods, 47(17), 4272-4285.
  • Das U., & Ebrahimi N. (2018). A new method for covariate selection in Cox model. Statistics in Transition New Series. 19(2), 297-314.
  • Das U., & Ebrahimi N. (2017). Covariate selection for accelerated failure time data. Communications in Statistics-Theory and Methods, 46(8), 4051-4064.
  • Das U. "Variable Selection for Survival Data under Weibull Distribution." Calcutta Statistical Association Bulletin 68.1-2 (2016): 52-68.
  • Das U., Gupta S., & Gupta S. (2014). Screening active factors in supersaturated designs. Computational Statistics & Data Analysis, 77, 223-232.
  • Foulkes A., Matthews G., Das U., Ferguson J., Lin R., Reilly M. (2013), Mixed Modeling of Meta-Analysis P-Values (MixMAP) Suggests Multiple Novel Gene Loci for Low Density Lipoprotein Cholesterol. PLoS ONE 8(2): e54812. doi:10.1371/journal.pone.0054812.
  • Pradhan V., Menon S. and Das U. (2013). Corrected profile likelihood confidence interval for binomial paired incomplete data. Pharmaceutical Statistics, vol 12: 48 - 58.
  • Das U., Maiti T., and Pradhan V. (2010). Bias correction of the logistic regression with missing categorical covariates. Journal of Statistical Planning and Inference, vol. 140: 2478 - 2485.
COURSES TAUGHT
  • Basic Statistics course (core course) for two years as well as one year programs
  • Elective course on analytics
  • Data science for business analytics (a new core analytics course in two years program)
  • Linear mixed models for practitioners (executive education)
  • Introducing R (executive education)