Anindya Bhadra

Associate Professor
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


Research Interests:


Editorial Activities:


Publications:

A. Selected Preprints:

[3] Sagar, K., Banerjee, S., Datta, J. and Bhadra, A. (2021+). Precision matrix estimation under the horseshoe-like prior–penalty dual. (submitted). [arXiv:2104.10750]

[2] Niu,Y., Guha, N., De, D., Bhadra, A., Baladandayuthapani, V. and Mallick, B. K. (2021+). Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures (submitted). [arXiv:2010.14638]

[1] Ma, P. and Bhadra, A. (2021+). Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions. (under revision). [arXiv:1911.05865]


B. Methodological Publications:

* equal contribution
g graduate student collaborator

[22] Bhadra, A. (2021). Invited discussion of "Bayesian Graphical Models for Modern Biological Applications" by Ni, Baladandayuthapani, Vannucci and Stingo. Statistical Methods & Applications (to appear).

[21] Li, Y.g, Datta, J., Craig, B. A. and Bhadra, A. (2021). Joint mean–covariance estimation via the horseshoe. Journal of Multivariate Analysis 183, 104716. [doi link] [MATLAB code]

[20] Bhadra, A., Datta, J., Li, Y.g and Polson, N. G. (2020). Horseshoe regularization for machine learning in complex and deep models (with discussion). International Statistical Review 88, 302–320. [doi link] [discussion 1] [discussion 2] [discussion 3] [discussion 4]

[19] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2020). Global-local mixtures: a unifying framework. Sankhya A (special issue in memory of Jayanta K. Ghosh) 82, 426–447. [doi link] [see also]

[18] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2020). The horseshoe-like regularization for feature subset selection. Sankhya B, special issue in memory of Jayanta K. Ghosh (to appear). [doi link]

[17] Bhadra, A., Datta, J., Polson, N. G. and Willard, B. (2019). Lasso meets horseshoe: a survey. Statistical Science 34, 405–427. [doi link]

[16] Bhadra, A., Datta, J., Li, Y.g, Polson, N. G. and Willard, B. (2019). Prediction risk for the horseshoe regression. Journal of Machine Learning Research 20(78), 1–39. [link]

[15] Li, Y.g, Craig, B. A. and Bhadra, A. (2019). The graphical horseshoe estimator for inverse covariance matrices. Journal of Computational and Graphical Statistics 28, 747–757. [doi link] [MATLAB code]

[14] Bhadra, A., Rao, A. and Baladandayuthapani, V. (2018). Inferring network structure in non-normal and mixed discrete-continuous genomic data. Biometrics 74, 185–195. [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. (2017). The horseshoe+ estimator of ultra-sparse signals. Bayesian Analysis 12, 1105–1131. [doi link] [see also] [Stan code]

[11] 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] [Stan code]

[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]


C. Applied Publications:

[13] Lin, L., Guo, J., Aqeel, M. M., Gelfand, S. B., Delp, E. J., Bhadra, A., Richards, E. A., Hennessy, E. and Eicher-Miller, H. A. (2021). Joint temporal dietary and physical activity patterns: associations with health status indicators and chronic diseases. American Journal of Clinical Nutrition (to appear).

[12] Cowan, A. E., Jun, S., Tooze, J. A., Dodd, K. W., Gahche, J. J., Eicher-Miller, H. A., Guenther, P. M., Dwyer, J. T., Potischman, N., Bhadra, A., Carroll, R. J. and Bailey, R. L. (2021). A narrative review of nutrient based indexes to assess diet quality and the proposed Total Nutrient Index that reflects total dietary exposures. Critical Reviews in Food Science and Nutrition (to appear). [doi link]

[11] Aqeel, M., Guo, J., Lin, L., Gelfand, S., Delp, E., Bhadra, A., Richards, E. A., Hennessy, E. and Eicher-Miller, H. A. (2021). Temporal Physical Activity Patterns are Associated with Obesity in U.S. Adults. Preventive Medicine 148, 106538. [doi link]

[10] Jun, S., Cowan, A. E., Dodd, K. W., Tooze, J. A., Gahche, J. J., Eicher-Miller, H. A., Guenther, P. M., Dwyer, J. T., Potischman, N., Bhadra, A., Forman, M. R., and Bailey, R. L. (2021). Association of food insecurity with dietary intakes and nutritional biomarkers among U.S. children, National Health and Nutrition Examination Survey (NHANES) 2011–2016. American Journal of Clinical Nutrition 114, 1059–1069. [doi link]

[9] Aqeel, M. M., Guo, J., Lin, L., Gelfand, S. B., Delp, E. J., Bhadra, A., Richards, E. A., Hennessy, E. and Eicher-Miller, H. A. (2020). Temporal Dietary Patterns are Associated with Obesity in U.S. Adults. Journal of Nutrition 150, 3259–3268. [doi link] [ASN press release]

[8] Jun, S., Cowan, A. E., Bhadra, A., Dodd, K. W., Dwyer, J. T., Eicher-Miller, H. A., Gahche, J., Guenther, P. M., Potischman, N., Tooze, J. A. and Bailey, R. L. (2020). Older adults with obesity have higher risks of some micronutrient inadequacies and lower overall dietary quality compared to peers with a healthy weight, National Health and Nutrition Examination Surveys (NHANES), 2011-2014. Public Health Nutrition 23, 2268–2279. [doi link]

[7] Aqeel, M., Forster, A., Richards, E. A., Hennessy, E., McGowan, B., Bhadra, A., Guo, J., Gelfand, S., Delp, E. and Eicher-Miller, H. A. (2020). The Effect of Timing of Exercise and Eating on Postprandial Response in Adults: A Systematic Review. Nutrients (Special Issue on Meal Timing to Improve Human Health) 12, 221. [doi link] [corrigendum]

[6] Eicher-Miller, H. A., Gelfand, S., Hwang, Y., Delp, E., Bhadra, A. and Guo, J. (2020). Distance metrics optimized for clustering temporal dietary patterning among U.S. adults. Appetite 144, 104451. [doi link]

[5] Cowan, A. E., Jun, S., Tooze, J. A., Eicher-Miller, H. A., Dodd, K. W., Gahche, J., J., Guenther, P. M., Dwyer, J. T., Potischman, N., Bhadra, A. and Bailey, R. L. (2020). Total Usual Micronutrient Intakes Compared to the Dietary Reference Intakes among U.S. Adults by Food Security Status. Nutrients (Special Issue on Nutrition among Vulnerable Populations) 12, 38. [doi link]

[4] Cowan, A. E., Jun, S., Tooze, J. A., Dodd, K. W., Dwyer, J. T., Eicher-Miller, H. A., Gahche, J., Guenther, P. M., Potischman, N., Bhadra, A. and Bailey, R. L. (2020). Comparison of four methods to assess the prevalence of use and estimates of usual nutrient intakes from dietary supplements among U.S. adults. Journal of Nutrition 150, 884–893. [doi link]

[3] Bailey, R. L., Dodd, K. W., Gahche, J. J., Dwyer, J. T., Cowan, A. E., Jun, S., Eicher-Miller, H. A., Guether, P. M., Bhadra, A., Thomas, P. R., Potischman, N., Carroll, R. J. and Tooze, J. A. (2019). Best Practices for Dietary Supplement Assessment and Estimation of Total Usual Nutrient Intakes in Population-Level Research and Monitoring. Journal of Nutrition 149, 181–197. [doi link] (Editor's Choice.)

[2] Jun, S., Cowan, A. E., Tooze, J. A., Gahche, J. J., Dwyer, J. T., Eicher-Miller, H. A., Bhadra, A., Guenther, P. M., Potischman, N., Dodd, K. W. and Bailey, R. L. (2018). Dietary Supplement Use among U.S. Children by Family Income, Food Security Level, and Nutrition Assistance Program Participation Status in 2011–2014. Nutrients (Special Issue on Advances in Dietary Supplements) 10, 1212. [doi link]

[1] Cowan, A. E., Jun, S., Gahche, J. J., Tooze, J. A., Dwyer, J. T., Eicher-Miller, H. A., Bhadra, A., Guenther, P. M., Potischman, N., Dodd, K. W. and Bailey, R. L. (2018). Dietary Supplement Use Differs by Socioeconomic and Health-Related Characteristics among U.S. Adults, NHANES 2011–2014. Nutrients (Special Issue on Advances in Dietary Supplements) 10, 1114. [doi link]


D. 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:

[4] DMS-2014371: Developments in Gaussian processes and beyond: applications in geostatistics and deep learning, National Science Foundation (NSF), 2020–2023. Role: PI.

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

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

[1] R21CA224764: Temporal dietary and physical activity patterns related to health outcomes, National Cancer Institute (NCI), 2018–2020. Role: Co-I.