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Journal: Journal of the Royal Statistical Society Series B: Statistical Methodology

Abbreviation

J. R. Stat. Soc. B

Publisher

Wiley-Blackwell

Journal Volumes

ISSN

1369-7412
0035-9246
1467-9868

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Publications 1 - 10 of 14
  • Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)
    Item type: Journal Article
    Beskos, Alexandros; Papaspiliopoulos, Omiros; Roberts, Gareth O.; et al. (2006)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Splines for financial volatility
    Item type: Journal Article
    Audrino, Francesco; Bühlmann, Peter (2009)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Testing against a high dimensional alternative
    Item type: Journal Article
    Goeman, Jelle J.; van de Geer, Sara; Houwelingen, Hans C. van (2006)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions
    Item type: Journal Article
    Robert, C. P.; Meng, X. L.; Moller, J.; et al. (2003)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Sure independence screening for ultrahigh dimensional feature space Discussion
    Item type: Journal Article
    Bickel, Peter; Buehlmann, Peter; Yao, Qiwei; et al. (2008)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Stability selection
    Item type: Journal Article
    Meinshausen, Nicolai; Bühlmann, Peter (2010)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Conditional transformation models
    Item type: Journal Article
    Hothorn, Torsten; Kneib, Thomas; Bühlmann, Peter (2014)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models
    Item type: Journal Article
    Henckel, Leonard; Perković, Emilija; Maathuis, Marloes H. (2022)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
    Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, that is, all covariate sets that can be used for this purpose. Different valid adjustment sets typically provide total effect estimates of varying accuracies. Restricting ourselves to causal linear models, we introduce a graphical criterion to compare the asymptotic variances provided by certain valid adjustment sets. We employ this result to develop two further graphical tools. First, we introduce a simple variance reducing pruning procedure for any given valid adjustment set. Second, we give a graphical characterization of a valid adjustment set that provides the optimal asymptotic variance among all valid adjustment sets. Our results depend only on the graphical structure and not on the specific error variances or edge coefficients of the underlying causal linear model. They can be applied to directed acyclic graphs (DAGs), completed partially directed acyclic graphs (CPDAGs) and maximally oriented partially directed acyclic graphs (maximal PDAGs). We present simulations and a real data example to support our results and show their practical applicability.
  • Regression shrinkage and selection via the Lasso: a retrospective (Robert Tibshirani)
    Item type: Other Journal Item
    Bühlmann, Peter (2011)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Group bound
    Item type: Journal Article
    Meinshausen, Nicolai (2015)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
Publications 1 - 10 of 14