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
E-mail: bhadra@purdue.edu
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


Publications:

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

[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 (to appear). [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
† corresponding author
g graduate student collaborator


B. Selected Pending Articles:

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

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

[3] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2016). Default Bayesian analysis with global-local shrinkage priors. (under revision). [arXiv:1510.03516] [additional simulations]

[2] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2016). The horseshoe+ estimator of ultra-sparse signals. (under revision). [arXiv:1502.00560] [additional simulations] [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]


Site Meter