{"id":379,"date":"2019-07-03T16:32:16","date_gmt":"2019-07-03T20:32:16","guid":{"rendered":"http:\/\/people.clas.ufl.edu\/kdkhare\/?page_id=379"},"modified":"2026-03-19T08:48:54","modified_gmt":"2026-03-19T12:48:54","slug":"grants","status":"publish","type":"page","link":"https:\/\/people.clas.ufl.edu\/kdkhare\/grants\/","title":{"rendered":"Grants"},"content":{"rendered":"\r\n
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NSF DMS-1106084:\u00a0Collaborative Research: Objective Bayesian Model Selection and Estimation in High Dimensional Statistical Models<\/strong><\/h2>\n\n\n\n

Publications:<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Rajaratnam, B. (2012). “Sparse matrix decompositions and graph characterizations,”\u00a0Linear Algebra and its Applications\u00a0437,\u00a02012, 932-947.<\/p>\n\n\n\n\n\n

Sang-Yun Oh, Onkar Dalal, Khare, K.<\/strong>, Bala Rajaratnam. (2014). “Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection,” Advances in\u00a0Neural Information Processing Systems (NIPS), 667-675.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong>, Oh, S. and Rajaratnam, B. (2015). “A convex pseudo-likelihood framework for high-dimensional partial correlation estimation withconvergence guarantees,”\u00a0Journal of the Royal Statistical Society B\u00a077, 803-825.<\/p>\n\n\n\n\n\n\n\n\n\n

NSF DMS-1511945 (joint with James Hobert, UF):\u00a0Development of New Approaches for Analysis of Markov Chain Monte Carlo Algorithms to Facilitate Principled Use of MCMC in Practice<\/strong><\/h2>\n\n\n\n

Publications (with Khare as co-author):<\/p>\n\n\n\n\n\n

Hobert, J. P. and Khare, K.<\/strong> (2015). “Computable upper bounds on the distance to stationarity for Jovanovski and Madras’s Gibbs sampler,”\u00a0Annales de la Faculte des Sciences de Toulouse, Mathematiques 24, 935-947.<\/p>\n\n\n\n\n\n

Chakraborty, S. and Khare, K.<\/strong> (2017). “Convergence properties of Gibbs samplers for Bayesian probit regression with proper priors,”\u00a0Electronic Journal of Statistics 11, 177-210.<\/p>\n\n\n\n\n\n

Pal, S., Khare, K.<\/strong> and Hobert, J.P. (2017). “Trace class Markov chains for Bayesian inference with generalized double Pareto shrinkage priors,”\u00a0Scandinavian Journal of Statistics 44, 307-323.<\/p>\n\n\n\n\n\n

Pal, S., Khare, K.<\/strong>, and Hobert, J.P. (2015). “Improving the Data Augmentation algorithm in the two-block setup,”\u00a0Journal of Computational and Graphical Statistics\u00a024, 1114-1133.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong>, Pal, S. and Su, Z. (2017). “A Bayesian approach for envelope models,”\u00a0Annals of Statistics\u00a045, 196-222.<\/p>\n\n\n\n\n\n

Rajaratnam, B, Sparks, D., Khare, K.<\/strong>, and Zhang, L. (2018). “Scalable\u00a0Bayesian shrinkage and uncertainty quantification for high-dimensional regression,” Journal of Computational and Graphical Statistics 28, 174-184.<\/p>\n\n\n\n\n\n

Ghosh, S., Khare, K.<\/strong> and Michailidis, G. (2019). “High dimensional\u00a0posterior consistency in Bayesian vector autoregressive models,” Journal\u00a0of the American Statistical Association 114, 735-748.<\/p>\n\n\n\n\n\n

Zhang, L., Khare, K.<\/strong> and Xing, Z. (2019). \u201cTrace class Markov chains for the Normal-Gamma Bayesian\u00a0shrinkage model\u201d, Electronic Journal of Statistics 13, 166-207.<\/p>\n\n\n\n\n\n\n\n\n\n

NSF-DMS 1821220 (joint with George Michailidis, UF): Statistical methodology for analysis and forecasting with large scale temporal data<\/strong><\/h2>\n\n\n\n

Publications (with Khare as co-author):<\/p>\n\n\n\n\n\n

Ghosh, S., Khare, K.<\/strong> and Michailidis, G. (2021). “Strong selection consistency of Bayesian vector autoregressive models based on a pseudo-likelihood approach,” Annals of Statistics 49, 1267-1299.
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Ghosh, S., Khare, K.<\/strong> and Michailidis, G. (2023). “The Bayesian Nested Lasso for Mixed Frequency Regression Models”, to appear in Annals of Applied<\/em> Statistics.<\/p>\n\n\n\n\n\n

Chakraborty, N., Khare, K.<\/strong> and Michailidis, G. (2023). “A Bayesian framework for sparse estimation in high-dimensional mixed frequency Vector Autoregressive models”, to appear in Statistica Sinica<\/em>.<\/p>\n\n\n\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t<\/div>\r\n<\/section>\r\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1004,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-379","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/pages\/379","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/users\/1004"}],"replies":[{"embeddable":true,"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/comments?post=379"}],"version-history":[{"count":9,"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/pages\/379\/revisions"}],"predecessor-version":[{"id":628,"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/pages\/379\/revisions\/628"}],"wp:attachment":[{"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/media?parent=379"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}