# Research Codes

Visit my Github page for computer codes and brief descriptions of several projects.

# Published and Accepted Articles

20. Gutierrez, R. and Guhaniyogi, R. (2021). Bayesian Dynamic Feature Partitioning in High-Dimensional Regression with Big Data. [full text] , *Accepted*

19. Spencer, D., Guhaniyogi, R. and Prado, R. (2020). Bayesian Mixed Effect Sparse Tensor Response Regression Model with Joint Estimation of Activation and Connectivity. *Psychometrika*, Accepted,
[full text]

18. Guhaniyogi, R. (2020). High Dimensional Bayesian Regularization in Regressions Involving Symmetric Tensors.*Accepted*, Proceedings of 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. [full text]

17. Guha, S. and Guhaniyogi, R. (2020). Bayesian Generalized Sparse Symmetric Tensor-on-Vector Regression.*Technometrics*, Accepted, [full text]

16. Guhaniyogi, R. (2020). Bayesian Methods for Tensor Regressions.*Wiley StatsRef: Statistical References Online*, Accepted, [full text]

15. Guhaniyogi, R. and Rodriguez, A. (2020). Joint Modeling of Longitudinal Relational Data and Exogenous Variables.*Bayesian Analysis*, 15 (2), 477-503. [full text]

14. Guhaniyogi, R. and Sanso, B. (2019). Large Multiscale Spatial Modeling using Tree Shrinkage Priors. *Statistica Sinica*, 30, 2023-2050. [full text]

13. Heaton, M.J., Datta, A., Finley, A., Furrer, R., Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., Lindgren, F., Nychka, D. W.,
Sun, F. and Mangion, A. Z. (in alphabetical order) (2019). Methods for Analyzing Large Spatial Data: A Review and Comparison. 24, 398-425. *Journal of Agricultural, Biological and Environmental Statistics* [full text]

12. Guhaniyogi, R. and Banerjee, S. (2019). Multivariate Spatial Meta Kriging. *Statistics and Probability Letters*, [full text]

11. Guhaniyogi, R. and Banerjee, S. (2018). Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets. *Technometrics*, 60(4), 430-444 [full text]
[code and details]

10. Guhaniyogi, R., Qamar, S. and Dunson, D.B. (2018). Bayesian Conditional Density Filtering. *Journal of Computational and Graphical Statistics*, 27(3), 653-672
[full text] [code and details]

9. Guhaniyogi, R., Qamar, S. and Dunson, D.B. (2017). Bayesian Tensor Regression. *Journal of Machine Learning Research*, 18, 1-31. [full text]
[code and details]

8. Guhaniyogi, R.(2017). Bayesian Nonparametric Areal Wombling for Small Scale Maps with an Application to Urinary
Bladder Cancer Data from Connecticut. *Statistics in Medicine*, DOI:10.1002/sim.7408 [full text]
[code and details]

7. Guhaniyogi, R. (2017). Convergence Rate of Bayesian Supervised Tensor Modeling with Multiway Shrinkage Priors. *Journal of Multivariate Analysis*, 160, 157-168 [full text]

6. Guhaniyogi, R. (2017). Multivariate Bias Adjusted Tapered Predictive Process Models. *Spatial Statistics*, 21, 42-65 [full text]
[code and details]

5. Guhaniyogi, R. and Dunson, D.B. (2016). Compressed Gaussian Process for Manifold Regression. *Journal of Machine Learning Research*, 17, 1-26. [full text]

4. Guhaniyogi, R. and Dunson, D.B. (2016). Bayesian Compressed Regression. *Journal of the American Statistical Association, Theory & Methods*, 110, 1500-1514. [full text]

3. Belani, H.K., Sekar, P., Guhaniyogi, R., Abraham, A., Bohjanen, P.R. and Bohjanen, K. (2014). Human papillomavirus vaccine acceptance among young men in Bangalore, India. *International Journal of Dermatology*, 53, 486-491. [full text]

2. Guhaniyogi, R., Finely, A.O., Banerjee, S. and Kobe, R. (2013).
Modeling Low-rank Spatially-Varying Cross-Covariances using Predictive Process with Application to Soil Nutrient Data. *Journal of Agricultural, Biological and Environmental Statistics*, 18, 274-298. [full text]

1. Guhaniyogi, R., Finely, A.O., Banerjee, S. and Gelfand, A.E. (2011). Adaptive Gaussian predictive process models for large spatial datasets.
*Environmetrics*, 22, 997-1007. [full text]

# Invited Discussions

Guhaniyogi, R. and Banerjee, S. (2012). Comment on "Article by Lum and Gelfand." *Bayesian Analysis*, 7, 59-62 [full text]

Guhaniyogi, R. and Banerjee, S. (2012). Discussion on "Inference for Size Demography from Point Pattern Data using Integral Projection Models."
*Journal of Agricultural, Biological and Environmental Statistics*, 17, 678-681 [full text]

# Articles under Review or in Revision

6. Guhaniyogi, R., Li, C., Savitsky, T. and Srivastava, S. (2021+). Distributed Bayesian Varying Coefficient Modeling
Using a Gaussian Process Prior. [full text] , *Under Review*

5. Guhaniyogi, R. (2021+). Privacy Preserving Efficient Computation in Bayesian High Dimensional Regression With Big Data Using Gaussian Scale Mixture Priors. [full text] , *Under Review*

4. Guhaniyogi, R., Spencer, D. (2021+). Bayesian Tensor Response Regression With an Application to
Brain Activation Studies. [full text] , *Accepted Subject to Minor Revision*

3. Guhaniyogi, R., Li, C., Savitsky, T. and Srivastava, S. (2021+). A Divide-and-Conquer Bayesian Approach to Large-Scale Kriging. [full text] , *Revision Requested*

2. Guha, S. and Guhaniyogi, R. (2021+). Bayesian Supervised Clustering of Undirected Networks with Applications to Brain Connectome Data. [full text] , *Under Review*

1. Guhaniyogi, R. and Guha, S. (2021+). Convergence Rate for Predictive Densities of Bayesian Generalized Scalar on Symmetric Tensor Regressions. [full text] , *Under Review*

# Book Chapters

Bhandari, S., Dutta, R. and Guhaniyogi, R. (2009). Study of Optimal Adaptive Rule in Testing Problems. Advances in Multivariate Statistical Methods (Chapter 16). Singapore: World Scientific. [full text]