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

Research Interests:


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

[14] Bhadra, A., Rao, A. and Baladandayuthapani, V. (2017). Inferring network structure in non-normal and mixed discrete-continuous genomic data. Biometrics (to appear). [doi link]

[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]

[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] [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:

[5] Bhadra, A., Datta, J., Li, Y., Polson, N. G. and Willard, B. (2017+). Prediction risk for the horseshoe regression. (submitted). [arXiv:1605.04796]

[4] Li, Y., Craig, B. A. and Bhadra, A. (2017+). The graphical horseshoe estimator for inverse covariance matrices. (submitted). [arXiv:1707.06661] [MATLAB code]

[3] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2017+). Lasso meets horseshoe. (submitted). [arXiv:1706.10179]

[2] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2017+). Horseshoe regularization for feature subset selection. (submitted). [arXiv:1702.07400]

[1] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2017+). Global-local mixtures. (submitted). [arXiv:1604.07487] [see also]

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:

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

[1] R01CA215834: Development of a total nutrient index, National Cancer Institute (NCI), 2017–2021. Role: Co-I.