Anindya Bhadra

Assistant Professor (August, 2012 - present)
Department of Statistics
Purdue University

Contact Information:
Office: MATH 518
Department of Statistics
Purdue University
250 N University St.
West Lafayette, IN 47907-2066
Phone: (765) 496-9551

Home | Curriculum Vitæ | Research | Presentations | Teaching | Software | Google Scholar

Education & Training:

Postdoctoral Fellow, Statistics, Texas A&M University, 2010 - 2012
Ph.D., Statistics, University of Michigan, 2010
M.A., Statistics, University of Michigan, 2007
B. Tech. (Honors), Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, 2004


A. Journal and Conference Articles (Published/Accepted):

[13] Bhadra, A. (2017). An expectation-maximization scheme for measurement error models. Statistics and Probability Letters 120, 61-68. [doi link]

[12] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2016). Default Bayesian analysis with global-local shrinkage priors. Biometrika 103, 955-969. [doi link] [additional simulations]

[11] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2016). The horseshoe+ estimator of ultra-sparse signals. Bayesian Analysis (to appear). [doi link] [additional simulations] [see also]

[10] Bhadra, A. and Carroll, R. J. (2016). Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems. Statistics and Computing 26, 827-840. [doi link]

[9] Bhadra, A. and Ionides, E. L. (2016). Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models. Statistics and Computing 26, 393-407. [doi link]

[8] Feldman, G.g, Bhadra, A. and Kirshner, S. (2014). Bayesian feature selection in high-dimensional regression in presence of correlated noise. Stat 3, 258-272. [doi link]

[7] Bhadra, A. and Baladandayuthapani, V. (2013). Integrative sparse Bayesian analysis of high-dimensional multi-platform genomic data in glioblastoma. 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2013), pp. 1-4. [doi link]

[6] Bhadra, A. and Mallick, B. K. (2013). Joint high-dimensional Bayesian variable and covariance selection with an application to eQTL analysis. Biometrics 69, 447-457. [doi link] (Biometrics June 2013 issue Highlights)

[5] Bhadra, A., Ionides, E. L., Laneri, K., Pascual, M., Bouma, M. and Dhiman, R. C.  (2011). Malaria in Northwest India: Data analysis via partially observed stochastic differential equation models driven by Lévy noise. Journal of the American Statistical Association 106, 440-451. [doi link] (One of the featured articles in JASA Applications & Case Studies, June 2011 issue.)

[4] Ionides, E. L., Bhadra, A., Atchadé, Y. and King, A. A. (2011). Iterated filtering.  Annals of Statistics 39, 1776-1802. [doi link]

[3] Bhadra, A. (2011). Invited discussion of "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by M. Girolami and B. Calderhead.  Journal of the Royal Statistical Society, Series B 73, 173-174. [doi link] [pdf]

[2] Laneri, K.*, Bhadra, A.*, Ionides, E. L., Bouma, M., Dhiman, R. C., Yadav, R. S. and Pascual, M. (2010). Forcing versus feedback: Epidemic malaria and monsoon rains in Northwest India. PLoS Computational Biology 6, e1000898. [doi link]  (Cover Article, September 2010 issue.)

[1] Bhadra, A. (2010). Contributed discussion of "Particle Markov chain Monte Carlo methods" by C. Andrieu, A. Doucet and R. Holenstein. Journal of the Royal Statistical Society, Series B 72, 314-315. [doi link] [pdf]

* equal contribution
g graduate student collaborator

B. Selected Pending Articles:

[3] Bhadra, A., Datta, J., Li, Y., Polson, N. G. and Willard, B. (2016). Prediction risk for global-local shrinkage regression. (submitted). [arXiv:1605.04796]

[2] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2016). Global-local mixtures. (submitted). [arXiv:1604.07487] [see also]

[1] Bhadra, A., Rao, A. and Baladandayuthapani, V. (2016). Inferring network structure in non-normal and mixed discrete-continuous genomic data. (under revision). [arXiv:1604.00376]

C. Theses:

[1] Bhadra, A. (2010). Time series analysis for nonlinear dynamical systems with applications to modeling of infectious diseases. Ph.D. dissertation, University of Michigan. [pdf]

External Grants:

[1] DMS-1613063: Bayesian global-local shrinkage in high dimensions, National Science Foundation (NSF), 2016-2019. Role: PI.

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