NSF DMS-1106084: Collaborative Research: Objective Bayesian Model Selection and Estimation in High Dimensional Statistical Models

Publications:

Khare, K. and Rajaratnam, B. (2012). “Sparse matrix decompositions and graph characterizations,” Linear Algebra and its Applications 437, 2012, 932-947.

Sang-Yun Oh, Onkar Dalal, Khare, K., Bala Rajaratnam. (2014). “Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection,” Advances in Neural Information Processing Systems (NIPS), 667-675.

Khare, K., Oh, S. and Rajaratnam, B. (2015). “A convex pseudo-likelihood framework for high-dimensional partial correlation estimation withconvergence guarantees,” Journal of the Royal Statistical Society B 77, 803-825.

 

NSF DMS-1511945 (joint with James Hobert, UF): Development of New Approaches for Analysis of Markov Chain Monte Carlo Algorithms to Facilitate Principled Use of MCMC in Practice

Publications (with Khare as co-author):

Hobert, J. P. and Khare, K. (2015). “Computable upper bounds on the distance to stationarity for Jovanovski and Madras’s Gibbs sampler,” Annales de la Faculte des Sciences de Toulouse, Mathematiques 24, 935-947.

Chakraborty, S. and Khare, K. (2017). “Convergence properties of Gibbs samplers for Bayesian probit regression with proper priors,” Electronic Journal of Statistics 11, 177-210.

Pal, S., Khare, K. and Hobert, J.P. (2017). “Trace class Markov chains for Bayesian inference with generalized double Pareto shrinkage priors,” Scandinavian Journal of Statistics 44, 307-323.

Pal, S., Khare, K., and Hobert, J.P. (2015). “Improving the Data Augmentation algorithm in the two-block setup,” Journal of Computational and Graphical Statistics 24, 1114-1133.

Khare, K., Pal, S. and Su, Z. (2017). “A Bayesian approach for envelope models,” Annals of Statistics 45, 196-222.

Rajaratnam, B, Sparks, D., Khare, K., and Zhang, L. (2018). “Scalable Bayesian shrinkage and uncertainty quantification for high-dimensional regression,” Journal of Computational and Graphical Statistics 28, 174-184.

Ghosh, S., Khare, K. and Michailidis, G. (2019). “High dimensional posterior consistency in Bayesian vector autoregressive models,” Journal of the American Statistical Association 114, 735-748.

Zhang, L., Khare, K. and Xing, Z. (2019). “Trace class Markov chains for the Normal-Gamma Bayesian shrinkage model”, Electronic Journal of Statistics 13, 166-207.

 

NSF-DMS 1821220 (joint with George Michailidis, UF): Statistical methodology for analysis and forecasting with large scale temporal data

Publications (with Khare as co-author):

Ghosh, S., Khare, K. and Michailidis, G. (2021). “Strong selection consistency of Bayesian vector autoregressive models based on a pseudo-likelihood approach,” Annals of Statistics 49, 1267-1299.

Ghosh, S., Khare, K. and Michailidis, G. (2023). “The Bayesian Nested Lasso for Mixed Frequency Regression Models”, to appear in Annals of Applied Statistics.

Chakraborty, N., Khare, K. and Michailidis, G. (2023). “A Bayesian framework for sparse estimation in high-dimensional mixed frequency Vector Autoregressive models”, to appear in Statistica Sinica.