Refereed publications

    Covariance estimation and high dimensional methodology

  • Ali, A., Khare, K., Oh, S. and Rajaratnam, B. (2017). Generalized pseudo-likelihood methods for inverse covariance estimation, Proceedings of Artificial Intelligence and Statistics (AISTATS).
  • Khare, K., Pal, S. and Su, Z. (2017). A Bayesian approach for envelope models, Annals of Statistics 45, 196-222.
  • 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.
  • Oh, S.. Dalal, O., Khare, K. and Rajaratnam, B. (2014). Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection, Proceedings of Neural Information Processing Systems (NIPS).
  • Khare, K. and Rajaratnam, B. (2012). Sparse matrix decompositions and graph characterizations, Linear Algebra and Its Applications 437, 932-947.
  • Khare, K. and Rajaratnam, B. (2011). Wishart distributions for decomposable covariance graph models, Annals of Statistics 39, 514-555.
  • 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.
  • MCMC

  • Pal, S., Khare, K. and Hobert, J.P. (2017). Trace class Markov chains for Bayesian inference with generalized double Pareto shrinkage priors, to appear in Scandinavian Journal of Statistics.
  • Mukherjee, N., Casella, G. and Khare, K. (2017). Algorithms for Improving Efficiency of Discrete Markov Chains, to appear in Indian Journal of Probability and Mathematics.
  • 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.
  • 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.
  • 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.
  • 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 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.
  • 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 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.
  • Bayesian asymptotics

  • 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.
  • Sparks, D., Khare, K. and Ghosh, M. (2014). Necessary and sufficient conditions for high-dimensional posterior consistency under g-priors, Bayesian Analysis 10, 627-664.
  • Dasgupta, S., Khare, K. and Ghosh, M. (2014). Asymptotic expansion of the posterior density in high dimensional generalized linear models, Journal of Multivariate Analysis 131, 126-148.
  • Interdisciplinary research

  • Matrinez, C.A., Rahman, S., Khare, K. and Elzo, M.A. (2017). Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction, to appear in Journal of Animal Breeding and Genetics.
  • Karalkar, N.B., Khare, K., Molt, R. and Benner, S.A. (2017). Tautomeric Equilibria of iso-Guanine and Related Purine Analogs, to appear in Nucleosides, Nucleotides and Nucleic Acids.
  • Vaziri, S., Abbatematteo, J.M., Wilson, J.M., Chakraborty, S., Khare, K., Kubilis, P.S., Hoh, D. (2017). Predictive performance of the American College of Surgeons Universal Risk Calculator in neurosurgical patients, to appear in Journal of Neurosurgery.
  • 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 amphetamine- induced motor behavior through a Rhes interaction network (Rhesactome) in the striatum, Science Signaling 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.