High dimensional covariance/network estimation and regression
Rahman, R., Khare, K., 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, to appear in Annals of Applied Statistics.
Khare, K. and Su, Z. (2023). Response variable selection for multivariate linear regression, to appear in Statistica Sinica.
Jalali, P., Khare, K. and Michailidis, G. (2023). A Bayesian approach to joint estimation of multiple graphical models, to appear in Statistica Sinica.
Samanta, S., Khare, K. and Michailidis, G. (2022). A generalized likelihood based Bayesian approach for scalable joint regression and covariance selection in high dimensions, Statistics and Computing 32, 47.
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.
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.
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., 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.
Bayesian Computation/MCMC
Mukherjee, S., Khare, K. and Chakraborty, S. (2023). Convergence properties of data augmentation algorithms for high-dimensional robit regression, Electronic Journal of Statistics 17, 19-69.
Zhou, J., Khare, K. and Srivastava, S. (2022). Asynchronous and distributed data augmentation for massive
data settings, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2022.2130928.
ADDENDUM – Proof ADDA and DA have same stationary distribution
Chakraborty, S., Bhattacharya, S. and Khare, K. (2022). Estimating accuracy of the MCMC variance estimator: Asymptotic normality for batch means estimators, Statistics and Probability Letters 183, 109337.
Bhattacharya, S., Khare, K. and Pal, S. (2022). Geometric ergodicity of Gibbs samplers for the Horseshoe and its regularized variants, Electronic Journal of Statistics 16, 1-57.
Backlund, G., Hobert, J.P., Jung, Y.J. and Khare, K. (2020). A hybrid scan Gibbs sampler for Bayesian models with latent variables, Statistical Science 36, 379-399.
Chakraborty, S. and Khare, K. (2019). “Consistent estimation of the spectrum of trace class data augmentation algorithms”, Bernoulli 25, 3832-3863.
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.
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.
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.
Rajaratnam, B., Sparks, D., Khare, K. and Zhang, L. (2018). Uncertainty quantification for modern high-dimensional regression via scalable Bayesian methods, Journal of Computational and Graphical Statistics 28, 174-184.
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 generalized double Pareto shrinkage priors, Scandinavian Journal of Statistics 44, 307-323.
Mukherjee, N., Casella, G. and Khare, K. (2017). Algorithms for Improving Efficiency of Discrete Markov Chains, Indian Journal of Probability and Mathematics 48, 495-511.
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 algorithms 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.
Methodology for mixed frequency data
Ghosh, S., Khare, K. and Michailidis, G. (2023). The Bayesian Nested Lasso for Mixed Frequency Regression Models, submitted.
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.
Bayesian high-dimensional asymptotics
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. (2019). High dimensional posterior consistency in Bayesian vector autoregressive models, Journal of the American Statistical Association 114, 735-748.
Cao, X., Khare, K. 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.
Cao, X., Khare, K. and Ghosh, M. (2019). “High-dimensional posterior consistency for hierarchical non- local priors in regression”, Bayesian Analysis 15, 241-262.
Cao, X., Khare, K. and Ghosh, M. (2018). Posterior graph selection and estimation consistency for high- dimensional Bayesian DAG models, Annals of Statistics 47, 319-348.
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 decomposable 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
Atanasova, K.R., Chakraborty, S., Ratnayake, R., Khare, K., Luesch, H. and Lele, T.P. (2022). An epigenetic small molecule screen to target abnormal nuclear morphology in human cells, to appear in Molecular Biology of the Cell.
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.
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, Journal of Animal Breeding and Genetics 134, 412-421.
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.
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, 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 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.