{"id":22,"date":"2012-09-05T11:22:33","date_gmt":"2012-09-05T15:22:33","guid":{"rendered":"https:\/\/people.clas.ufl.edu\/template\/?page_id=22"},"modified":"2026-03-19T08:48:55","modified_gmt":"2026-03-19T12:48:55","slug":"research","status":"publish","type":"page","link":"https:\/\/people.clas.ufl.edu\/kdkhare\/research\/","title":{"rendered":"Research"},"content":{"rendered":"\r\n
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Research<\/h1>\n\n\n\n

High dimensional covariance\/network estimation and regression<\/h3>\n\n\n\n\n\n\n\n\n\n

Yang, Z., Khare, K.<\/strong> and Michailidis, G. (2025+). Bayesian methodology for adaptive sparsity and shrinkage in regression, to appear in the Journal of Business and Economic Statistics<\/em>.<\/p>\n\n\n\n\n\n

Jalali, P., Khare, K.<\/strong> and Michailidis, G. (2025+). B-CONCORD – A scalable Bayesian high-dimensional precision matrix estimation procedure, to appear in Indian Journal of Probability and Mathematics.<\/em><\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Su, Z. (2024). Response variable selection for multivariate linear regression, Statistica Sinica<\/em> 34<\/strong>, 1325-1345 doi:10.5705\/ss.202022.0127.<\/p>\n\n\n\n

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Rahman, R., Khare, K.<\/strong>, Michailidis, G., Martinez, C. and Carulla, J. (2023). Estimation of Gaussian directed acyclic graphs using partial ordering information with an application to dairy cattle data, Annals of Applied Statistics\u00a0<\/em>17<\/strong>, 929-960.<\/p>

Jalali, P., Khare, K.<\/strong> and Michailidis, G. (2023). A Bayesian approach to joint estimation of multiple graphical models, Statistica Sinica <\/em>33<\/strong>, <\/em>2669-2692.<\/p>

Samanta, S., Khare, K.<\/strong> and Michailidis, G. (2022). A generalized likelihood based Bayesian approach for scalable joint regression and covariance selection in high dimensions, Statistics and Computing<\/em> 32<\/strong>, 47.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

Khare, K.<\/strong>, Oh, S., Rahman, S. and Rajaratnam, B. (2019). A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data, <\/span>Machine Learning<\/em> 108, 2061-2086.\u00a0<\/span><\/p>

Khare, K<\/strong>., Rajaratnam, B. and Saha, A. (2018). Bayesian inference for Gaussian graphical models beyond decomposable graphs, <\/span>Journal of the Royal Statistical Society, Series B<\/em> 80, 727-747.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

Ali, A., Khare, K.<\/strong>, Oh, S. and Rajaratnam, B. (2017). Generalized pseudo-likelihood methods for inverse covariance estimation, Proceedings of Artificial Intelligence and Statistics (AISTATS)<\/em>.<\/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, Journal of the Royal Statistical Society B<\/em> 77, 803-825.<\/p>\n\n\n\n\n\n

Oh, S.. Dalal, O., Khare, K.<\/strong> and Rajaratnam, B. (2014). Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection, Proceedings of Neural Information Processing Systems (NIPS)<\/em>.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Rajaratnam, B. (2012). Sparse matrix decompositions and graph characterizations, Linear Algebra and Its Applications<\/em> 437, 932-947.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Rajaratnam, B. (2011). Wishart distributions for decomposable covariance graph models, Annals of Statistics<\/em> 39, 514-555.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Rajaratnam, B. (2010). Covariance trees and related Wishart distributions, AMS CONM Volume, Algebraic Methods in Statistics and Probability II<\/em>, Editors M.Viana and H.Wynn.<\/p>\n\n\n\n\n\n\n\n\n\n

Bayesian Computation\/MCMC<\/h3>\n\n\n\n\n\n\n\n\n\n

Hobert, J. P. and Khare, K.<\/strong> (2024). Recurrence and transience of a Markov chain on Z + and evaluation of prior distributions for a Poisson mean, Journal of Applied Probability<\/em> 61<\/strong>, 1361-1379.<\/p>\n\n\n\n\n\n

Mukherjee, S., Khare, K. <\/strong>and Chakraborty, S. (2023). Convergence properties of data augmentation algorithms for high-dimensional robit regression, Electronic Journal of Statistics 17, 19-69.
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Zhou, J., Khare, K.<\/strong> and Srivastava, S. (2022). Asynchronous and distributed data augmentation for massive
\ndata settings, Journal of Computational and Graphical Statistics<\/em>, DOI: 10.1080\/10618600.2022.2130928<\/a><\/span>.<\/p>\n\n\n\n\n\n

ADDENDUM – Proof ADDA and DA have same stationary distribution<\/a><\/p>\n\n\n\n

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Chakraborty, S., Bhattacharya, S. and Khare, K.<\/strong> (2022). Estimating accuracy of the MCMC variance estimator: Asymptotic normality for batch means estimators, Statistics and Probability Letters <\/em>183, 109337.
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Bhattacharya, S., Khare, K.<\/strong> and Pal, S. (2022). Geometric ergodicity of Gibbs samplers for the Horseshoe and its regularized variants, Electronic Journal of Statistics <\/em>16, 1-57. <\/em><\/p>

Backlund, G., Hobert, J.P., Jung, Y.J. and Khare, K.<\/strong> (2020). A hybrid scan Gibbs sampler for Bayesian models with latent variables, Statistical Science<\/em> 36, 379-399.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

Chakraborty, S. and Khare, K.<\/strong> (2019). \u201cConsistent estimation of the spectrum of trace class data augmentation algorithms\u201d, Bernoulli<\/em> 25, 3832-3863.<\/p>\n

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

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Qin, Q., Hobert, J. and Khare, K.<\/strong> (2019). \u201cEstimating the spectral gap of a trace-class Markov operator\u201d, Electronic Journal of Statistics<\/em> 13, 1790-1822.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

Hobert, J.P., Jung, Y.J., Khare, K.<\/strong> and Qin, Q. (2018). Convergence analysis of MCMC algorithms for Bayesian multivariate linear regression with non-Gaussian errors, Scandinavian Journal of Statistics<\/em> 45, 513-533.\u00a0<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

Rajaratnam, B., Sparks, D., Khare, K.<\/strong> and Zhang, L. (2018). Uncertainty quantification for modern high-dimensional regression via scalable Bayesian methods, Journal of Computational and Graphical Statistics<\/em> 28, 174-184.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong>, Pal, S. and Su, Z. (2017). A Bayesian approach for envelope models, Annals of Statistics<\/em> 45, 196-222.<\/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, Scandinavian Journal of Statistics<\/em> 44, 307-323.<\/p>\n\n\n\n\n\n

Mukherjee, N., Casella, G. and Khare, K.<\/strong> (2017). Algorithms for Improving Efficiency of Discrete Markov Chains, Indian Journal of Probability and Mathematics<\/em> 48, 495-511.<\/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, Electronic Journal of Statistics<\/em> 11, 177-210.<\/p>\n\n\n\n\n\n

Hobert, J.P. and Khare, K.<\/strong> (2016). Discussion of “Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood” by Yang, Wang and He, International Statistical Review<\/em> 84, 349-356.<\/p>\n\n\n\n\n\n

Pal, S., Khare, K.<\/strong>, and Hobert, S. (2015). Improving the Data Augmentation algorithm in the two-block setup, Journal of Computational and Graphical Statistics<\/em> 24, 1114-1133.<\/p>\n\n\n\n\n\n

Hobert, J. and Khare, K.<\/strong> (2015). Computable upper bounds on the distance to stationarity for Jovanovski and Madrass Gibbs sampler, Annales de la Faculte des Sciences de Toulouse<\/em> (special Persi Diaconis issue) 24, 935-947.<\/p>\n\n\n\n\n\n

Pal, S. and Khare, K.<\/strong> (2014). Geometric ergodicity for Bayesian shrinkage models, Electronic Journal of Statistics<\/em> 8, 604-645.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Hobert, J. P. (2013). Geometric ergodicity of the Bayesian lasso, Electronic Journal of Statistics<\/em> 7, 2150-2163.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Mukherjee, N. (2013). Convergence analysis of some multivariate Markov chains using stochastic monotonicity, Annals of Applied Probability<\/em> 23, 811-833.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Hobert, J. P. (2012). Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression, Journal of Multivariate Analysis<\/em> 112, 108-116.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Hobert, J. P. (2011). A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants, Annals of Statistics<\/em> 39, 2585-2606.<\/p>\n\n\n\n\n\n

Diaconis, P., Khare, K.<\/strong> and Saloff-Coste, L. (2010). Stochastic alternating projections, Illinois Journal of Mathematics<\/em> 54, 963-979.<\/p>\n\n\n\n\n\n

Diaconis, P., Khare, K.<\/strong> and Saloff-Coste, L. (2010). Gibbs sampling, conjugate priors and coupling, Sankhya Ser. A<\/em> 72, 136-169.<\/p>\n\n\n\n\n\n

Khare, K.<\/strong> and Zhou, H. (2009). Rates of convergence of some multivariate Markov chains with polynomial eigenfunctions, Annals of Applied Probability<\/em> 19, 737-777.<\/p>\n\n\n\n\n\n

Diaconis, P., Khare, K.<\/strong> and Saloff-Coste, L. (2008). Gibbs sampling, exponential families and orthogonal polynomials (with discussion), Statistical Science<\/em> 23, 151-178.<\/p>\n\n\n\n\n\n\n\n\n\n

Methodology for mixed frequency data<\/h3>\n\n\n\n\n\n\n\n\n\n

Chakraborty, N., Khare, K.<\/strong> and Michailidis, G. (2025+). Bayesian group-shrinkage based estimation in panel VAR models with mixed frequency data, to appear in the Annals of Applied Statistics<\/em>.<\/p>\n\n\n\n\n\n

Ghosh, S., Khare, K.<\/strong> and Michailidis, G. (2023). The Bayesian Nested Lasso for Mixed Frequency Regression Models, Annals of Applied Statistics <\/em>17<\/strong>, 2279-2304.<\/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, Statistica Sinica\u00a0<\/em>33<\/strong>, 1629-1652.<\/p>\n\n\n\n\n\n\n\n\n\n

Bayesian high-dimensional asymptotics<\/h3>\n\n\n\n\n\n\n\n\n\n

Sarkar, P., Khare, K.<\/strong> and Ghosh, M. (2025+). High-dimensional Posterior Consistency in Multi-response Regression models with Non-informative Priors for Error Covariance Matrix, to appear in Bernoulli.\u00a0<\/em><\/p>\n\n\n\n

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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 <\/em>49, 1267-1299.
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Ghosh, S., Khare, K.<\/strong> and Michailidis, G. (2019). High dimensional posterior consistency in Bayesian vector autoregressive models, Journal of the American Statistical Association<\/em> 114, 735-748.<\/p>

Cao, X., Khare, K.<\/strong> and Ghosh, M. (2019). Consistent Bayesian sparsity selection for high-dimensional Gaussian DAG models with multiplicative and beta-mixture priors, to appear in Journal of Multivariate Analysis<\/em>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

Cao, X., Khare, K.<\/strong> and Ghosh, M. (2019). \u201cHigh-dimensional posterior consistency for hierarchical non- local priors in regression\u201d, Bayesian Analysis<\/em> 15, 241-262.<\/p>\n\n\n\n

Cao, X., Khare, K.<\/strong> and Ghosh, M. (2018). Posterior graph selection and estimation consistency for high- dimensional Bayesian DAG models,\u00a0 Annals of Statistics<\/em> 47, 319-348.<\/p>\n\n\n\n\n\n

Xiang, R., Ghosh, M. and Khare, K.<\/strong> (2016). Consistency of Bayes factors under hyper g-priors with growing model size, Journal of Statistical Planning and Inference<\/em> 173, 64-86.<\/p>\n\n\n\n\n\n

Xiang, R., Khare, K.<\/strong> and Ghosh, M. (2015). High dimensional posterior convergence rates for decomposable graphical models, Electronic Journal of Statistics<\/em> 9, 2828-2854.<\/p>\n\n\n\n\n\n

Sparks, D., Khare, K.<\/strong> and Ghosh, M. (2014). Necessary and sufficient conditions for high-dimensional posterior consistency under g-priors, Bayesian Analysis<\/em> 10, 627-664.<\/p>\n\n\n\n\n\n

Dasgupta, S., Khare, K.<\/strong> and Ghosh, M. (2014). Asymptotic expansion of the posterior density in high dimensional generalized linear models, Journal of Multivariate Analysis<\/em> 131, 126-148.<\/p>\n\n\n\n\n\n\n\n\n\n

Interdisciplinary research<\/h3>\n\n\n\n\n\n\n\n\n\n

Atanasova, K.R., Chakraborty, S., Ratnayake, R., Khare,<\/b> K.<\/strong>, Luesch, H. and Lele, T.P. (2022).\u00a0An epigenetic small molecule screen to target abnormal nuclear morphology in human cells,\u00a0to appear in Molecular Biology of the Cell<\/em>.<\/p>\n\n\n\n

Vaziri, S., Awan, O., Porche, K., Scott, K., Sacks, P., Dru, A.B., Chakraborty, S., Khare, K.<\/strong>, Hoh, B., and Rahman, M. (2019). Reimbursement Patterns for Neurosurgery: Analysis of the NERVES Survey Results from 2011-2016, Clinical Neurology and Neurosurgery.\u00a0<\/em><\/p>\n\n\n\n

Martinez, C.A., Khare, K.<\/strong>, Rahman, S. and Elzo, M.A. (2018). Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models, Journal of Theoretical Biology<\/em> 437, 67-78.<\/p>\n\n\n\n\n\n

Matrinez, C.A., Rahman, S., Khare, K.<\/strong> and Elzo, M.A. (2017). Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction, Journal of Animal Breeding and Genetics<\/em> 134, 412-421.<\/p>\n\n\n\n\n\n

Karalkar, N.B., Khare, K.<\/strong>, Molt, R. and Benner, S.A. (2017). Tautomeric Equilibria of iso-Guanine and Related Purine Analogs, Nucleosides, Nucleotides and Nucleic Acids<\/em> 36, 256-274.<\/p>\n\n\n\n\n\n

Vaziri, S., Abbatematteo, J.M., Wilson, J.M., Chakraborty, S., Khare, K.<\/strong>, Kubilis, P.S., Hoh, D. (2017). Predictive performance of the American College of Surgeons Universal Risk Calculator in neurosurgical patients, Journal of Neurosurgery\u00a0<\/em>128, 942-947.<\/p>\n\n\n\n\n\n

Martinez, C.A., Khare, K.<\/strong>, Banerjee, A. and Elzo, M.A. (2017). Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. I: Multivariate Gaussian priors for marker effects and derivation of the joint probability mass function of genotypes, Journal of Theoretical Biology<\/em> 417, 8-19.<\/p>\n\n\n\n\n\n

Martinez, C.A., Khare, K.<\/strong>, Banerjee, A. and Elzo, M.A. (2017). Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. II: Multivariate spike and slab priors for marker effects and derivation of approximate Bayes and fractional Bayes factors for the complete family of models, Journal of Theoretical Biology<\/em> 417, 131-141.<\/p>\n\n\n\n\n\n

Shahani, N., Swarnkar, S., Giovinazzo, V., Morgenweck, J., Bohn, L.M., Scharager-Tapia, C., Pascal, B., Martinez-Acedo, P., Khare, K.<\/strong> and Subramaniam, S. (2016). RasGRP1 promotes amphetamine- induced motor behavior through a Rhes interaction network (Rhesactome) in the striatum, Science Signaling<\/em> 9, RA111.<\/p>\n\n\n\n\n\n

Martinez, C., Khare, K.<\/strong> and Enzo, M. (2015). On the Bayesness, minimaxity, and admissibility of point estimators of allelic frequencies, Journal of Theoretical Biology<\/em> 383, 106-115.<\/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-22","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/pages\/22","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=22"}],"version-history":[{"count":10,"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/pages\/22\/revisions"}],"predecessor-version":[{"id":644,"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/pages\/22\/revisions\/644"}],"wp:attachment":[{"href":"https:\/\/people.clas.ufl.edu\/kdkhare\/wp-json\/wp\/v2\/media?parent=22"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}