Uncertainty Quantification for the Horseshoe (with Discussion)
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Aad van der Vaart | St'ephanie van der Pas | Botond Szab'o | A. V. D. Vaart | Botond Szab'o | S. V. D. Pas | A. Vaart
[1] D. Fraser,et al. Bayes, Reproducibility and the Quest for Truth , 2016 .
[2] A. W. van der Vaart,et al. Adaptive Bayesian credible bands in regression with a Gaussian process prior , 2015, Sankhya A.
[3] Prasenjit Ghosh,et al. Asymptotic Optimality of One-Group Shrinkage Priors in Sparse High-dimensional Problems , 2017 .
[4] Arnaud Doucet,et al. Sparse Bayesian nonparametric regression , 2008, ICML '08.
[5] A. V. D. Vaart,et al. BAYESIAN LINEAR REGRESSION WITH SPARSE PRIORS , 2014, 1403.0735.
[6] D. Picard,et al. Adaptive confidence interval for pointwise curve estimation , 2000 .
[7] D. A. S. Fraser. Is Bayes Posterior just Quick and Dirty Confidence , 2011 .
[8] N. Pillai,et al. Dirichlet–Laplace Priors for Optimal Shrinkage , 2014, Journal of the American Statistical Association.
[9] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[10] Sara van de Geer,et al. Statistics for High-Dimensional Data , 2011 .
[11] J. Griffin,et al. Inference with normal-gamma prior distributions in regression problems , 2010 .
[12] Michael I. Jordan,et al. Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces , 2004, J. Mach. Learn. Res..
[13] R. Nickl,et al. A sharp adaptive confidence ball for self-similar functions , 2014, 1406.3994.
[14] Van Der Vaart,et al. The Horseshoe Estimator: Posterior Concentration around Nearly Black Vectors , 2014, 1404.0202.
[15] Nicholas G. Polson,et al. The Horseshoe+ Estimator of Ultra-Sparse Signals , 2015, 1502.00560.
[16] Cun-Hui Zhang,et al. Confidence intervals for low dimensional parameters in high dimensional linear models , 2011, 1110.2563.
[17] Malay Ghosh,et al. Asymptotic Properties of Bayes Risk of a General Class of Shrinkage Priors in Multiple Hypothesis Testing Under Sparsity , 2013, 1310.7462.
[18] Adam D. Bull,et al. Honest adaptive confidence bands and self-similar functions , 2011, 1110.4985.
[19] Bin Yu,et al. Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression , 2013, 1306.5505.
[20] Wenhua Jiang,et al. General maximum likelihood empirical Bayes estimation of normal means , 2009, 0908.1709.
[21] S. Geer,et al. On asymptotically optimal confidence regions and tests for high-dimensional models , 2013, 1303.0518.
[22] Enes Makalic,et al. A Simple Sampler for the Horseshoe Estimator , 2015, IEEE Signal Processing Letters.
[23] Harry van Zanten,et al. Honest Bayesian confidence sets for the L2-norm , 2013, 1311.7474.
[24] Jayanta K. Ghosh,et al. Asymptotic Properties of Bayes Risk for the Horseshoe Prior , 2013 .
[25] Stephen G. Walker,et al. Empirical Bayes posterior concentration in sparse high-dimensional linear models , 2014, 1406.7718.
[26] Stephen G. Walker,et al. Asymptotically minimax empirical Bayes estimation of a sparse normal mean vector , 2013, 1304.7366.
[27] I. Johnstone,et al. Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences , 2004, math/0410088.
[28] James G. Scott,et al. Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction , 2022 .
[29] James G. Scott,et al. On the half-cauchy prior for a global scale parameter , 2011, 1104.4937.
[30] Prasenjit Ghosh,et al. Posterior Concentration Properties of a General Class of Shrinkage Priors around Nearly Black Vectors , 2014 .
[31] V. Johnson,et al. On the use of non‐local prior densities in Bayesian hypothesis tests , 2010 .
[32] James G. Scott,et al. The horseshoe estimator for sparse signals , 2010 .
[33] Aad van der Vaart,et al. Adaptive posterior contraction rates for the horseshoe , 2017, 1702.03698.
[34] S. Geer,et al. The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso) , 2011 .
[35] Eduard Belitser,et al. Needles and straw in a haystack: robust empirical Bayes confidence for possibly sparse sequences , 2015 .
[36] Jiunn T. Hwang,et al. The Nonexistence of 100$(1 - \alpha)$% Confidence Sets of Finite Expected Diameter in Errors-in-Variables and Related Models , 1987 .
[37] Sara van de Geer,et al. Confidence sets in sparse regression , 2012, 1209.1508.
[38] A. W. Vaart,et al. Frequentist coverage of adaptive nonparametric Bayesian credible sets , 2013, 1310.4489.
[39] Ker-Chau Li,et al. Honest Confidence Regions for Nonparametric Regression , 1989 .
[40] Tatyana Krivobokova,et al. Adaptive empirical Bayesian smoothing splines , 2014, 1411.6860.
[41] J. Robins,et al. Adaptive nonparametric confidence sets , 2006, math/0605473.
[42] James G. Scott. Bayesian Estimation of Intensity Surfaces on the Sphere via Needlet Shrinkage and Selection , 2011 .
[43] James G. Scott,et al. Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors , 2010, 1010.5223.
[44] James G. Scott. Parameter expansion in local-shrinkage models , 2010, 1010.5265.
[45] R. Nickl,et al. On the Bernstein–von Mises phenomenon for nonparametric Bayes procedures , 2013, 1310.2484.
[46] R. Nickl,et al. CONFIDENCE BANDS IN DENSITY ESTIMATION , 2010, 1002.4801.
[47] Jaeyong Lee,et al. GENERALIZED DOUBLE PARETO SHRINKAGE. , 2011, Statistica Sinica.
[48] A. V. D. Vaart,et al. Needles and Straw in a Haystack: Posterior concentration for possibly sparse sequences , 2012, 1211.1197.
[49] J. Rousseau,et al. Asymptotic frequentist coverage properties of Bayesian credible sets for sieve priors , 2016, The Annals of Statistics.
[50] Eduard Belitser,et al. Needles and straw in a haystack: Robust confidence for possibly sparse sequences , 2015, Bernoulli.
[51] A. V. D. Vaart,et al. Credible sets in the fixed design model with Brownian motion prior , 2015 .
[52] James G. Scott,et al. Handling Sparsity via the Horseshoe , 2009, AISTATS.
[53] Kolyan Ray. Adaptive Bernstein–von Mises theorems in Gaussian white noise , 2014, 1407.3397.
[54] Empirical priors for targeted posterior concentration rates , 2016, 1604.05734.