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During the past decade, shrinkage priors have received much attention in Bayesian analysis of high-dimensional data.
Hybrid monte carlo
Duane, S., Kennedy, A., Pendleton, B., and Roweth, D · 1987
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Variable selection via gibbs sampling
George, E. I., and McCulloch, R. E · 1993
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Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
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Weak Convergence and Empirical Processes
Van der Vaart, A., and Wellner, J · 1996
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Information-theoretic characterization of bayes performance and the choice of priors in parametric and nonparametric problems. In: Bernardo, J., Berger, J., Dawid, A., and Smith, A., eds
Barron, A · 1998
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Asymptotic normality of posterior distributions in high-dimensional linear models
Ghosal, S · 1999
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Convergence rates of posterior distributions
Ghosal, S., Ghosh, J. K., and Van Der Vaart, A. W · 2000
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Variable selection via nonconcave penalized likelihood and its oracle properties
Fan, J., and Li, R · 2001
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Large-scale simultaneous hypothesis testing: the choice of a null hypothesis
Efron, B · 2004
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Spike and slab variable selection: frequentist and bayesian strategies
Ishwaran, H., and Rao, J · 2005
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Regularization and variable selection via the elastic net
Zou, H., and Hastie, T · 2005
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Homozygosity mapping with snp arrays identifies trim32, an e3 ubiquitin ligase, as a bardet–biedl syndrome gene (bbs11)
Chiang, A. P., Beck, J. S., Yen, H.-J., Tayeh, M. K., Scheetz, T. E., Swiderski, R. E., Nishimura, D. Y., Braun, T. A., Kim, K.-Y. A., and Huang, J · 2006
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Misspecification in infinite-dimensional bayesian statistics
Kleijn, B., and van der Vaart, A · 2006
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Regulation of gene expression in the mammalian eye and its relevance to eye disease
Scheetz, T. E., Kim, K.-Y. A., Swiderski, R. E., Philp, A. R., Braun, T. A., Knudtson, K. L., Dorrance, A. M., DiBona, G. F., Huang, J., Casavant, T. L., Sheffield, V. C., and Stone, E. M · 2006
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Convergence rates of posterior distributions for noniid observations
Ghosal, S., and Van Der Vaart, A. W · 2007
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Bayesian variable selection for high dimensional generalized linear models: Convergence rate of the fitted densities
Jiang, W · 2007
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Extended bayesian information criteria for model selection with large model spaces
Chen, J., and Chen, Z · 2008
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Estimating fdr under general dependence using stochastic approximation
Liang, F., and Zhang, J · 2008
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The bayesian lasso
Park, T., and Casella, G · 2008
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The sparsity and bias of the lasso selection in high-dimensional regression
Zhang, C.-H., and Huang, J · 2008
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Bayesian lasso regression
Hans, C · 2009
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The horseshoe estimator for sparse signals
Carvalho, C., Polson, N., and Scott, J · 2010
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Regularization paths for generalized linear models via coordinate descent
Friedman, J., Hastie, T., and Tibshirani, R · 2010
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Inequalities for quantiles of the chi-square distribution
Inglot, T · 2010
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Bayes and empirical-bayes multiplicity adjustment in the variable-selection problem
Scott, J., and Berger, J · 2010
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Nearly unbiased variable selection under minimax concave penalty
Zhang, C.-H · 2010
Cited alongside, same era.
Generalized beta mixtures of gaussians
Armagan, A., Clyde, M., and Dunson, D. B · 2011
Cited alongside, same era.
Bernstein–von mises theorems for gaussian regression with increasing number of regressors
Bontemps, D · 2011
Cited alongside, same era.
Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
Breheny, P., and Huang, J · 2011
Cited alongside, same era.
Riemann manifold langevin and hamiltonian monte carlo methods
Girolami, M., and Galderhead, B · 2011
Cited alongside, same era.
Bayesian hyper-lassos with non-convex penalization
Griffin, J. E., and Brown, P. J · 2011
Cited alongside, same era.
Confidence intervals for low dimensional parameters in high dimensional linear models
Zhang, C.-H., and Zhang, S · 2014
Later among the works it cites.
Dirichlet-laplace priors for optimal shrinkage
Bhattacharya, A., Pati, D., Pillai, N. S., and Dunson, D. B · 2015
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High-dimensional inference: Confidence intervals, p-values and r-software hdi
Dezeure, R., Bühlmann, P., Meier, L., and Meinshausen, N · 2015
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Decoupling shrinkage and selection in bayesian linear models: A posterior summary perspective
Hahn, P., and Carvalho, C · 2015
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The flare package for high dimensional linear regression and precision matrix estimation in r
Li, X., Zhao, T., Yuan, X., and Liu, H · 2015
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High dimensional variable selection with reciprocal l 1 l_{1} -regularization
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Mcmc using hamiltonian dynamics In: Brooks, S., Gelman, A., Jones, G., and Meng, X.-L., eds
Neal, R · 2011
Cited alongside, same era.
Minimax rates of estimation for high-dimensional linear regression over l q l_{q} -balls
Raskutti, G., Wainwright, M. J., and Yu, B · 2011
Cited alongside, same era.
Bayesian learning via stochastic gradient langevin dynamics
Welling, M., and Teh, Y. W · 2011
Cited alongside, same era.
Structuring shrinkage: some correlated priors for regression
Griffin, J., and Brown, P · 2012
Cited alongside, same era.
Bayesian model selection in high-dimensional settings
Johnson, V., and Rossel, D · 2012
Cited alongside, same era.
Introduction to the non-asymptotic analysis of random matrices In: Eldar, Y., and Kutyniok, G., eds
Vershynin, R · 2012
Cited alongside, same era.
Song, Q., and Liang, F · 2015
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Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth
Narisetty, N · 2016
Later among the works it cites.
Exact post-selection inference for sequential regression procedures
Tibshirani, R. J., Taylor, J., Lockhart, R., and Tibshirani, R · 2016
Later among the works it cites.
On the computational complexity of high-dimensional bayesian variable selection
Yang, Y., Wainwright, M. J., and Jordan, M. I · 2016
Later among the works it cites.
Variable selection using shrinkage priors
Li, H., and Pati, D · 2017
Closest in time.
Empirical bayes posterior concentration in sparse high-dimensional linear models
Martin, R., Mess, R., and Walker, S. G · 2017
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Adaptive posterior contraction rates for the horseshoe
van der Pas, S., Szabo, B., and van der Vaart, A · 2017
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Uncertainty quantification for the horseshoe
van der Pas, S., Szabo, B., and van der Vaart, A · 2017
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Bayesian sparse global-local shrinkage regression for grouped variables
Xu, Z., Schmidt, D., Makalic, E., Qian, G., and Hopper, J · 2017
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Bayesian linear regression with sparse priors
Castillo, I., Schmidt-Hieber, J., and van der Vaart, A · 2018
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The spike-and-slab lasso
Ročková, V., and George, E. I · 2018
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Optimal false discovery control of minimax estimator
Song, Q., and Cheng, G · 2018
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Bayesian variable selection and estimation based on global-local shrinkage priors
Tang, X., Xu, X., Ghosh, M., and Ghosh, P · 2018
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Prediction risk for the horseshoe regression
Bhadra, A., Datta, J., Li, Y., Polson, N., and Willard, B. T · 2019
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Empirical bayes oracle uncertainty quantification for regression
Belitser, E., and Ghosal, S · 2020
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A general framework for bayes structured linear models
Gao, C., van der Vaart, A. W., and Zhou, H. H · 2020
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Bayesian shrinkage towards sharp minimaxity
Song, Q · 2020
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Sparse bayesian additive nonparametric regression with application to health effects of pesticides mixtures
Wei, R., Reich, B. J., Hoppin, J. A., and Ghosal, S · 2020
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On the beta prime prior for scale parameters in high-dimensional bayesian regression models
Bai, R., and Ghosh, M · 2021
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Needles and straw in a haystack: Posterior concentration for possibly sparse sequences
Castillo, I., and van der Vaart, A · 2069
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