Works matching IS 13697412 AND DT 2022 AND VI 84 AND IP 1
Results: 18
High‐dimensional quantile regression: Convolution smoothing and concave regularization.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 205, doi. 10.1111/rssb.12485
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Transfer learning for high‐dimensional linear regression: Prediction, estimation and minimax optimality.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 149, doi. 10.1111/rssb.12479
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A graph‐theoretic approach to randomization tests of causal effects under general interference.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 174, doi. 10.1111/rssb.12478
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Jorge Mateu's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 47, doi. 10.1111/rssb.12461
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- Article
Inferential Wasserstein generative adversarial networks.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 83, doi. 10.1111/rssb.12476
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Waste‐free sequential Monte Carlo.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 114, doi. 10.1111/rssb.12475
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Authors' reply to the Discussion of 'Gaussian Differential Privacy' by Dong et al.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 50, doi. 10.1111/rssb.12463
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Priyantha Wijayatunga's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 49, doi. 10.1111/rssb.12462
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- Article
Usable and precise asymptotics for generalized linear mixed model analysis and design.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 55, doi. 10.1111/rssb.12473
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J. Goseling and M.N.M. van Lieshout's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 46, doi. 10.1111/rssb.12460
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- Article
Sebastian Dietz's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 44, doi. 10.1111/rssb.12459
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- Article
Christine P. Chai's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 43, doi. 10.1111/rssb.12458
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- Article
Peter Krusches and Frank Bretz's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 42, doi. 10.1111/rssb.12457
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Seconder of the vote of thanks to Dong et al. and contribution to the Discussion of 'Gaussian Differential Privacy'.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 39, doi. 10.1111/rssb.12456
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- Article
Proposer of the vote of thanks to Dong et al. and contribution to the Discussion of 'Gaussian Differential Privacy'.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 37, doi. 10.1111/rssb.12455
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- Article
Gaussian differential privacy.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 3, doi. 10.1111/rssb.12454
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- Article
High‐dimensional, multiscale online changepoint detection.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 234, doi. 10.1111/rssb.12447
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Issue Information.
- Published in:
- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022, v. 84, n. 1, p. 1, doi. 10.1111/rssb.12433
- Publication type:
- Article