Anindya Bhadra

Anindya Bhadra Professor Department of Statistics University Faculty Scholar 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

[8] Fang, X. and Bhadra, A. (2024+). Posterior Concentration for Gaussian Process Priors under Rescaled Matérn and Confluent Hypergeometric Covariance Functions. (submitted). [arXiv:2312.07502]

[7] Yarger, D. and Bhadra, A. (2024+). On Valid Multivariate Generalizations of the Confluent Hypergeometric Covariance Function. (submitted). [arXiv:2312.05682] [R code]

[6] Yao, T.-H., Ni, Y., Bhadra, A., Kang, J. and Baladandayuthapani, V. (2024+). Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks. (submitted). [arXiv:2310.18474] [R code]

[5] Loría, J.^g and Bhadra, A. (2024+). Posterior Inference on Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance. (submitted). [arXiv:2305.10664] [R code]

[4] Sagar, K.^g, Ni, Y., Baladandayuthapani, V. and Bhadra, A. (2024+). Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference. (submitted, winner of a IISA 2022 Best Poster Award for K. Sagar). [arXiv:2210.08096] [R code]

[2] Chakraborty, M., Baladandayuthapani, V., Bhadra, A. and Ha, M. J. (2024+). Bayesian Robust Learning in Chain Graph Models for Integrative Pharmacogenomics. (under revision). [arXiv:2111.11529]

[1] Niu,Y., Guha, N., De, D., Bhadra, A., Baladandayuthapani, V. and Mallick, B. K. (2024+). Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures. (submitted). [arXiv:2010.14638]

[28] Sagar, K.^g, Datta, J., Banerjee, S. and Bhadra, A. (2024). Maximum a posteriori estimation in graphical models using local linear approximation. Stat (to appear). [arXiv:2303.06914] [MATLAB code]

[27] Bhadra, A., Datta, J., Polson, N. G., Sokolov, V. and Xu, J. (2024). Merging Two Cultures: Deep and Statistical Learning. Wiley Interdisciplinary Reviews: Computational Statistics 16, e1647. [doi link]

[26] Loría, J.^g and Bhadra, A. (2024). SURE-tuned Bridge Regression. Statistics and Computing 34, 30. [doi link] [R code]

[25] Sagar, K.^g, Banerjee, S., Datta, J. and Bhadra, A. (2024). Precision matrix estimation under the horseshoe-like prior–penalty dual. Electronic Journal of Statistics 18, 1–46. [doi link] [MATLAB code] (Winner of a 2022 ENAR Distinguished Student Paper Award for K. Sagar.)

[24] Ma, P. and Bhadra, A. (2023). Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions. Journal of the American Statistical Association 118, 2045–2058. [doi link]

[23] Sagar, K.^g and Bhadra, A. (2022). A Laplace Mixture Representation of the Horseshoe and Some Implications. IEEE Signal Processing Letters 29, 2547–2551. [doi link] [R code]

[22] Bhadra, A. (2022). Invited discussion of "Bayesian Graphical Models for Modern Biological Applications" by Ni, Baladandayuthapani, Vannucci and Stingo. Statistical Methods & Applications 31, 235–239. [doi link]

[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., Polson, N. G. and Willard, B. (2021). The horseshoe-like regularization for feature subset selection. Sankhya B (special issue in memory of Jayanta K. Ghosh) 83, 185–214. [doi link]

[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., 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]

[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:

[20] Lin, L., Guo, J., Gelfand, S., Bhadra, A., Delp, E., Richards, E., Hennessy, E. and Eicher-Miller, H. (2024). Temporal Dietary Pattern Cluster Membership Varies on Weekdays and Weekends but Both Link to Health. Journal of Nutrition 154, 722–733. [doi link]

[19] Lin, L., Guo, J., Bhadra, A., Gelfand, S. B., Delp, E. J., Richards, E. A., Hennessy, E. and Eicher-Miller, H. A. (2023). Temporal Patterns of Diet and Physical Activity and of Diet Alone Have More Numerous Relationships with Health and Disease Status Indicators Compared to Temporal Patterns of Physical Activity Alone. Journal of the Academy of Nutrition and Dietetics 123, 1729–1748. [doi link]

[18] Guo, J., Aqeel, M., Lin, L., Gelfand, S., Eicher-Miller, H., Bhadra, A., Hennessy, E., Richards, E. and Delp, E. (2023). Joint Temporal Patterns By Integrating Diet and Physical Activity. IEEE International Conference on Digital Health (ICDH 2023), pp. 13–23. [doi link]

[17] Guo, J., Aqeel, M., Lin, L., Gelfand, S., Eicher-Miller, H., Bhadra, A., Hennessy, E., Richards, E. and Delp, E. (2023). Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults. IEEE International Conference on Healthcare Informatics (ICHI 2023), pp. 214–224. [doi link]

[16] 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. (2023). 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 63, 1722–1732. [doi link]

[15] Cowan, A. E., Tooze, J. A., Gahche, J. J., Eicher-Miller, H. A., Guenther, P. M., Dwyer, J. T., Potischman, N., Bhadra, A., Carroll, R. J. and Bailey, R. L. (2022). Trends in overall and micronutrient-containing dietary supplement use among U.S. adults and children, NHANES 2007-2018. Journal of Nutrition 152, 2789–2801. [doi link]

[14] Lin, L., Guo, J., Li, Y., Gelfand, S. B., Delp, E. J., Bhadra, A., Richards, E. A., Hennessy, E. and Eicher-Miller, H. A. (2022). The discovery of data-driven temporal dietary patterns and a validation of their description using energy and time cut-offs. Nutrients (Special Issue on Dietary Surveys and Nutritional Epidemiology) 14, 3483. [doi link]

[13] Cowan, A. E., Bailey, R. L., Jun, S., Dodd, K. W., Gahche, J. J., Eicher-Miller, H. A., Guenther, P. M., Dwyer, J. T., Potischman, N., Bhadra, A., Carroll, R. J. and Tooze, J. A. (2022). The Total Nutrient Index is a useful measure for assessing total micronutrient exposures among U. S. adults. Journal of Nutrition 152, 863–871. [doi link]

[12] 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. (2022). Joint temporal dietary and physical activity patterns: associations with health status indicators and chronic diseases. American Journal of Clinical Nutrition 115, 456–470. [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.