Certicate of Merit award in recognition of superior academic achievement in the Department of Mathematical Sciences, Northern Illinois University.
ARTICLES PUBLISHED/UNDER REVISION
Das, U., Mitra D. and Das, K. Analysis of interval censored competing risks data when some causes are missing. Under review.
Lahiri, S.N., Das, U. & Nordman, D.J. (2019). Empirical Likelihood for a long range dependent process subordinated to a Gaussian process. Accepted in Journal of Time Series Analysis.
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. VOLUME 19 , ISSUE 2 , Pages 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, Ujjwal. "Variable Selection for Survival Data under Weibull Distribution." Calcutta Statistical Association Bulletin 68.1-2 (2016): 52-68.
"Bias Reduction in Logistic Regression with Missing Responses when the Missing Data Mechanism is Nonignorable" by Maity A.K., Pradhan V. and Das, U. Accepted in "The American Statistician" doi 10.1080/00031305.2017.1407359.
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.