Faculty Directory A to Z

Faculty

Ujjwal Das

Operations Management, Quantitative Methods and Information Systems

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

ujjwal.das@iimu.ac.in

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)