Publications

(*I update this page sporadically. Please refer to my CV or my Google scholar profile for a current list.)

Statistics (Journal):

  1. The Horseshoe+ Estimator of Ultra-Sparse Signals“, Bayesian Analysis, Bhadra, Datta, Polson, and Willard. (2017)  (alphabetical order). arXiv and software (featured on Andrew Gelman’s blog: Bayesian survival analysis with horseshoe priors – in Stan! Statistical Modeling, Causal Inference, and Social Science).
  2. “Bayesian inference on quasi-sparse count data”. Biometrika 103 (4): 971-983. Datta and Dunson (2016), R markdown pages (Simulation, Slice sampling R, and Stan codes
  3. Default Bayesian analysis with global-local shrinkage priors“, Biometrika 103 (4): 955-969. Bhadra, Datta, Polson, and Willard. (2016) (alphabetical order)arXiv and software.
  4. “Bootstrap : An Exploration”, Statistical Methodology – Special Issue in Memory of Kesar Singh. Datta and Ghosh (2014).
  5. “Asymptotic Properties of Bayes Risk for the Horseshoe prior”, Bayesian Analysis, 8.1 (2013):111-132.Datta and Ghosh (2013), link.

Interdisciplinary Collaboration:

A. Cancer Genomics / Human Genetics : 
  1.  “Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma.” Cell 171.2: 481-494. Reddy, Anupama, et al. (2017)
  2. Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2 , Journal of Experimental Medicine, 214(5), 1371-86, Moffitt et al. (2017).
  3. The Genetic Basis of Hepatosplenic T Cell Lymphoma, Cancer Discovery, CD-16-0330, McKinney et al. (2017).
  4. GNA13 loss in germinal center B cells leads to impaired apoptosis and GC-B cell persistence and promotes lymphoma in vivo“. Blood,127 (22), 2723-2731, Healy et al. (2016). Link
  5. “Integrative Genetic and Clinical Analysis through Whole Exome Sequencing in 1001 Diffuse Large B Cell Lymphoma (DLBCL) Patients Reveals Novel Disease Drivers and Risk Groups”. Blood, 128 (22), 1087. Zhang et al. (2016). 
  6. “SETD2 Functional Loss through Mutation or Genetic Deletion Promotes Expansion of Normal and Malignant γδ T Cells through Loss of Tumor Suppressor Function and Upregulation of Oncogenic Pathways”. Blood128 (22):1052; McKinney et al. (2016). 
B. Other Disciplines:
  1. Evaluation of malnutrition as a predictor of adverse outcomes in febrile neutropenia associated with pediatric hematological malignancies.” Journal of Paediatrics and Child Health, 52 (7), 704-709.Chaudhuri, Biswas, Datta, …, Chakarabrty. (2016)
  2. Age-related changes in the relationship between auditory brainstem responses and envelope-following responses. Journal of the Association for Research in Otolaryngology, 15(4): 649-661. Parthasarathy, Datta, Torres, Hopkins, Bartlett (2014), Springer US.
  3. “Geomorphons: Landform and property predictions in a glacial moraine in Indiana landscapes”Catena 2016 v.142. Libohova, , Winzeler, Lee, Schoeneberger, Datta, and Owens, Phillip R. (2016).
     

Book Chapters

  1. In Search of Optimal Objective Priors for Model Selection and Estimation“. Current Trends in Bayesian Methodology with Applications, edited by Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, Appaia Loganathan, CRC Press. Datta and Ghosh (2015)
  2. Some Remarks on Pseudo Panel Data”. Growth Curve and Structural Equation Modeling, pp. 25-34. Springer International Publishing. Dasgupta, Ghosh, Chakravarty, and Datta, (2015).

Articles under Review

  1.  “Horseshoe Regularization for Feature Subset Selection“. Bhadra, Datta, Polson, and Willard, Brandon (2017+), (alphabetical order), arXiv.
  2.  “Prediction risk for global-local shrinkage regression“.Bhadra, Datta, Polson, and Willard, Brandon (2017+), (alphabetical order) arXiv .
  3. “Lasso Meets Horseshoe – A Survey”, Bhadra, Datta, Polson, and Willard, Brandon (2017+), (alphabetical order). arXiv
  4.  “Global-local mixtures“, Bhadra, Datta, Polson, and Willard (2017+), (authors in alphabetical order) arXiv. See Prof. Christian Robert’s blog entry: Blog: Global-local mixtures , Featured on Xi’an’s Og !

Articles in Preparation

  1. Nonparametric Bayes multiresolution testing for massive-dimensional rare events“. Datta, Dave and Dunson (2017+).
  2. A statistical method for drawing robust inferences in the presence of local dependence in genome-scale data“. Majumder Partha P., and Datta, Jyotishka.
  3. Covariance Selection using Continuous Shrinkage Priors“. Datta, Jyotishka, and Bhadra, Anindya.
  4. An Empirical Bayes Approach to Power Calculation and Cross-validation in Multiple Testing“. Datta, Jyotishka (2016+),

 

Others:

  1. “Does Machine Learning Reduce Racial Disparities in Policing? Datta, Jyotishka and Drawve, Grant, International Indian Statistical Association Newsletter, December 2017.
  2. Optimal Objective Priors for Linear Models”, Datta, Jyotishka. Indian Bayesian Society Newsletter, May 2014.