Kshitij Khare Basic Information Mailing Address: Telephone Numbers: Internet: Department of Statistics Office: (352) 273-2985 E-mail: [email protected]fl.edu 103 Griffin Floyd Hall FAX: (352) 392-5175 Web: http://www.stat.ufl.edu/˜kdkhare/ University of Florida Gainesville, FL 32611 Education PhD in Statistics, 2009, Stanford University (Advisor: Persi Diaconis) Masters in Mathematical Finance, 2009, Stanford University Masters in Statistics, 2004, Indian Statistical Institute, India Bachelors in Statistics, 2002, Indian Statistical Institute, India Academic Appointments University of Florida: Associate Professor of Statistics, 2015-present University of Florida: Assistant Professor of Statistics, 2009-2015 Stanford University: Research/Teaching Assistant, Department of Statistics, 2004-2009 Research Interests High-dimensional covariance/network estimation using graphical models High-dimensional inference for vector autoregressive models Markov chain Monte Carlo methods Kshitij Khare 2 Publications Core Statistics Research 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. Khare, K., Oh, S., Rahman, S. and Rajaratnam, B. (2019). A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data, Machine Learning 108, 2061-2086. Cao, X., Khare, K. and Ghosh, M. (2019). “High-dimensional posterior consistency for hierarchical non- local priors in regression”, Bayesian Analysis 15, 241-262. Chakraborty, S. and Khare, K. (2019). “Consistent estimation of the spectrum of trace class data augmen- tation algorithms”, Bernoulli 25, 3832-3863. Cao, X., Khare, K. and Ghosh, M. (2019). “Posterior graph selection and estimation consistency for high- dimensional Bayesian DAG models”, Annals of Statistics 47, 319-348. Qin, Q., Hobert, J. and Khare, K. (2019). “Estimating the spectral gap of a trace-class Markov operator”, Electronic Journal of Statistics 13, 1790-1822. 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. Sparks, D., Rajaratnam, B., 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. Khare, K., Rajaratnam, B. and Saha, A. (2018). Bayesian inference for Gaussian graphical models beyond decomposable graphs, Journal of the Royal Statistical Society, Series B 80, 727-747. Hobert, J.P., Jung, Y.J., Khare, K. and Qin, Q. (2018). Convergence analysis of MCMC algorithms for Bayesian multivariate linear regression with non-Gaussian errors, Scandinavian Journal of Statistics 45, 513-533. Khare, K., Pal, S. and Su, Z. (2017). A Bayesian approach for envelope models, Annals of Statistics 45, 196-222. Pal, S., Khare, K. and Hobert, J.P. (2017). Trace class Markov chains for Bayesian inference with general- ized double Pareto shrinkage priors, Scandinavian Journal of Statistics 44, 307-323. Ali, A., Khare, K., Oh, S. and Rajaratnam, B. (2017). Generalized pseudo-likelihood methods for inverse Kshitij Khare 3 covariance estimation, Proceedings of Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida. 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. Hobert, J.P. and Khare, K. (2016). Discussion of “Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood” by Yang, Wang and He, International Statistical Review 84, 349-356. Xiang, R., Ghosh, M. and Khare, K. (2016). Consistency of Bayes factors under hyper g-priors with growing model size, Journal of Statistical Planning and Inference 173, 64-86. Xiang, R., Khare, K. and Ghosh, M. (2015). High dimensional posterior convergence rates for decompos- able graphical models, Electronic Journal of Statistics 9, 2828-2854. Pal, S., Khare, K., and Hobert, S. (2015). Improving the Data Augmentation algorithm in the two-block setup, Journal of Computational and Graphical Statistics 24, 1114-1133. Hobert, J. and Khare, K. (2015). Computable upper bounds on the distance to stationarity for Jovanovski and Madrass Gibbs sampler, Annales de la Faculte des Sciences de Toulouse (special Persi Diaconis issue) 24, 935-947. Khare, K., Oh, S., and Rajaratnam, B. (2015). A convex pseudo-likelihood framework for high dimensional partial correlation estimation, Journal of the Royal Statistical Society B 77, 803-825. Sparks, D., Khare, K. and Ghosh, M. (2015). Necessary and sufficient conditions for high-dimensional posterior consistency under g-priors, Bayesian Analysis 10, 627-664. Oh, S., Dalal, O., Khare, K. and Rajaratnam, B. (2014). “Optimization Methods for Sparse Pseudo- Likelihood Graphical Model Selection”, Advacnes in Neural Information Processing Systems 2014, 667-675. Dasgupta, S., Khare, K., and Ghosh, M. (2014). “Asymptotic expansion of the posterior density in high dimensional generalized linear models”, J. Multivariate Analysis 131, 126-148. Pal, S. and Khare, K. (2014). Geometric ergodicity for Bayesian shrinkage models, Electronic Journal of Statistics 8, 604-645. Khare, K. and Hobert, J. P. (2013). Geometric ergodicity of the Bayesian lasso, Electronic Journal of Statistics 7, 2150-2163. Khare, K. and Mukherjee, N. (2013). Convergence analysis of some multivariate Markov chains using stochastic monotonicity, Annals of Applied Probability 23, 811-833. Kshitij Khare 4 Khare, K. and Hobert, J. P. (2012). Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression, Journal of Multivariate Analysis 112, 108-116. Khare, K. and Rajaratnam, B. (2012). Sparse matrix decompositions and graph characterizations, Linear Algebra and Its Applications 437, 932-947. Khare, K. and Hobert, J. P. (2011). A spectral analytic comparison of trace-class data augmentation algo- rithms and their sandwich variants, Annals of Statistics 39, 2585-2606. Khare, K. and Rajaratnam, B. (2011). Wishart distributions for decomposable covariance graph models, Annals of Statistics 39, 514-555. Diaconis, P., Khare, K. and Saloff-Coste, L. (2010). Stochastic alternating projections, Illinois Journal of Mathematics 54, 963-979. Diaconis, P., Khare, K. and Saloff-Coste, L. (2010). Gibbs sampling, conjugate priors and coupling, Sankhya Ser. A 72, 136-169. Khare, K. and Rajaratnam, B. (2010). Covariance trees and related Wishart distributions, AMS CONM Volume, Algebraic Methods in Statistics and Probability II, Editors M.Viana and H.Wynn. Khare, K. and Zhou, H. (2009). Rates of convergence of some multivariate Markov chains with polynomial eigenfunctions, Annals of Applied Probability 19, 737-777. Diaconis, P., Khare, K. and Saloff-Coste, L. (2008). Gibbs sampling, exponential families and orthogonal polynomials (with discussion), Statistical Science 23, 151-178. Interdisciplinary Research Vaziri, S., Awan, O., Porche, K., Scott, K., Sacks, P., Dru, A.B., Chakraborty, S., Khare, K., Hoh, B., and Rahman, M. (2019). Reimbursement Patterns for Neurosurgery: Analysis of the NERVES Survey Results from 2011-2016, Clinical Neurology and Neurosurgery. Martinez, C.A., Khare, K., Rahman, S. and Elzo, M.A. (2018). Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models, Journal of Theoretical Biology 437, 67-78 Karalkar, N.B., Khare, K., Molt, R. and Benner, S.A. (2017). Tautomeric Equilibria of iso-Guanine and Related Purine Analogs, Nucleosides, Nucleotides and Nucleic Acids 36, 256-274. Martnez, C.A., Khare, K., Rahman, S., and Elzo, M.A. (2017). Gaussian covariance graph models account- ing for correlated marker effects in genome-wide prediction, Journal of Animal Breeding and Genetics 134, 412-421. Kshitij Khare 5 Vaziri, S., Abbatematteo, J.M., Wilson, J.M., Chakraborty, S., Khare, K., Kubilis, P.S., Hoh, D. (2018). Pre- dictive performance of the American College of Surgeons Universal Risk Calculator in neurosurgical patients, Journal of Neurosurgery 128, 942-947. Martinez, C.A., Khare, K., 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 417, 8-19. Martinez, C.A., Khare, K., 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 417, 131-141. Shahani, N., Swarnkar, S., Giovinazzo, V., Morgenweck, J., Bohn, L.M., Scharager-Tapia, C., Pascal, B., Martinez-Acedo, P., Khare, K. and Subramaniam, S. (2016). RasGRP1 promotes amphetaminein- duced motor behavior through a Rhes interaction network (Rhesactome) in the striatum, Science Sig- naling 9, RA111 Martinez, C., Khare, K. and Enzo, M. (2015). On the Bayesness, minimaxity, and admissibility of point estimators of allelic frequencies, Journal of Theoretical Biology 383, 106-115. Papers under review Ghosh, S., Khare, K. and Michailidis, G. (2020). “Strong selection consistency of Bayesian vector au- toregressive