Found: 63
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Plug‐in machine learning for partially linear mixed‐effects models with repeated measurements.
- Published in:
- Scandinavian Journal of Statistics, 2023, v. 50, n. 4, p. 1553, doi. 10.1111/sjos.12639
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- Article
ricu: R's interface to intensive care data.
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- GigaScience, 2023, v. 12, n. 1, p. 1, doi. 10.1093/gigascience/giad041
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- Article
Random Forests for Change Point Detection.
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- Journal of Machine Learning Research, 2023, v. 24, p. 1
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- Article
Confidence and Uncertainty Assessment for Distributional Random Forests.
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- Journal of Machine Learning Research, 2023, v. 24, p. 1
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- Article
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.
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- WIREs: Data Mining & Knowledge Discovery, 2023, v. 13, n. 1, p. 1, doi. 10.1002/widm.1479
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- Article
Identifying cancer pathway dysregulations using differential causal effects.
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- Bioinformatics, 2022, v. 38, n. 6, p. 1550, doi. 10.1093/bioinformatics/btab847
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- Article
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.
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- Journal of Machine Learning Research, 2022, v. 23, p. 1
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- Article
Structure Learning for Directed Trees.
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- Journal of Machine Learning Research, 2022, v. 23, p. 1
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- Article
The Weighted Generalised Covariance Measure.
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- Journal of Machine Learning Research, 2022, v. 23, p. 1
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- Article
Anchor regression: Heterogeneous data meet causality.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2021, v. 83, n. 2, p. 215, doi. 10.1111/rssb.12398
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- Article
Domain adaptation under structural causal models.
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- Journal of Machine Learning Research, 2021, v. 22, p. 1
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- Article
Deconfounding and Causal Regularisation for Stability and External Validity.
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- International Statistical Review, 2020, v. 88, p. S114, doi. 10.1111/insr.12426
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- Article
Spectral Deconfounding via Perturbed Sparse Linear Models.
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- Journal of Machine Learning Research, 2020, v. 20, p. 1
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- Article
Goodness‐of‐fit testing in high dimensional generalized linear models.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2020, v. 82, n. 3, p. 773, doi. 10.1111/rssb.12371
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- Article
Hierarchical inference for genome-wide association studies: a view on methodology with software.
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- Computational Statistics, 2020, v. 35, n. 1, p. 1, doi. 10.1007/s00180-019-00939-2
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- Article
Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software.
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- 2020
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- Letter
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise.
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- Journal of Machine Learning Research, 2019, v. 20, n. 137-159, p. 1
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- Article
Most Likely Transformations.
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- Scandinavian Journal of Statistics, 2018, v. 45, n. 1, p. 110, doi. 10.1111/sjos.12291
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- Article
Kernel-based tests for joint independence.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2018, v. 80, n. 1, p. 5, doi. 10.1111/rssb.12235
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- Article
Goodness-of-fit tests for high dimensional linear models.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2018, v. 80, n. 1, p. 113, doi. 10.1111/rssb.12234
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- Article
Causal inference by using invariant prediction: identification and confidence intervals.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2016, v. 78, n. 5, p. 947, doi. 10.1111/rssb.12167
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- Article
A multi-marker association method for genome-wide association studies without the need for population structure correction.
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- Nature Communications, 2016, v. 7, n. 11, p. 13299, doi. 10.1038/ncomms13299
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- Article
Assessing statistical significance in multivariable genome wide association analysis.
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- Bioinformatics, 2016, v. 32, n. 13, p. 1990, doi. 10.1093/bioinformatics/btw128
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- Article
Discussion on ‘regularized regression for categorical data (Tutz and Gertheiss)’.
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- Statistical Modelling: An International Journal, 2016, v. 16, n. 3, p. 205, doi. 10.1177/1471082X16642566
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- Article
A Sequential Rejection Testing Method for High-Dimensional Regression with Correlated Variables.
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- International Journal of Biostatistics, 2016, v. 12, n. 1, p. 79, doi. 10.1515/ijb-2015-0008
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- Article
Arabidopsis GERANYLGERANYL DIPHOSPHATE SYNTHASE 11 is a hub isozyme required for the production of most photosynthesis-related isoprenoids.
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- New Phytologist, 2016, v. 209, n. 1, p. 252, doi. 10.1111/nph.13580
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- Article
Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2015, v. 77, n. 1, p. 291, doi. 10.1111/rssb.12071
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- Article
Hypersurfaces and Their Singularities in Partial Correlation Testing.
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- Foundations of Computational Mathematics, 2014, v. 14, n. 5, p. 1079, doi. 10.1007/s10208-014-9205-0
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- Article
High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation.
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- Journal of Machine Learning Research, 2014, v. 15, p. 3065
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- Article
High-dimensional variable screening and bias in subsequent inference, with an empirical comparison.
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- Computational Statistics, 2014, v. 29, n. 3/4, p. 407, doi. 10.1007/s00180-013-0436-3
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- Article
Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems.
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- Journal of Machine Learning Research, 2014, v. 15, n. 6, p. 1903
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- Article
Conditional transformation models.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2014, v. 76, n. 1, p. 3, doi. 10.1111/rssb.12017
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- Article
Controlling false positive selections in high-dimensional regression and causal inference.
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- Statistical Methods in Medical Research, 2013, v. 22, n. 5, p. 466, doi. 10.1177/0962280211428371
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- Article
Causal statistical inference in high dimensions.
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- Mathematical Methods of Operations Research, 2013, v. 77, n. 3, p. 357, doi. 10.1007/s00186-012-0404-7
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- Article
Causal stability ranking.
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- Bioinformatics, 2012, v. 28, n. 21, p. 2819, doi. 10.1093/bioinformatics/bts523
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- Article
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs.
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- Journal of Machine Learning Research, 2012, v. 13, n. 8, p. 2409
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- Article
MissForest—non-parametric missing value imputation for mixed-type data.
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- Bioinformatics, 2012, v. 28, n. 1, p. 112, doi. 10.1093/bioinformatics/btr597
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- Article
Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome.
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- BMC Cancer, 2012, v. 12, n. 1, p. 310, doi. 10.1186/1471-2407-12-310
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- Article
Systems‐based analysis of Arabidopsis leaf growth reveals adaptation to water deficit.
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- Molecular Systems Biology, 2012, v. 8, n. 1, p. 1, doi. 10.1038/msb.2012.39
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- Article
High-dimensional Covariance Estimation Based On Gaussian Graphical Models.
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- Journal of Machine Learning Research, 2011, v. 12, n. 10, p. 2975
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- Article
Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ.
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- Scandinavian Journal of Statistics, 2011, v. 38, n. 2, p. 197, doi. 10.1111/j.1467-9469.2011.00740.x
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- Article
Stability selection.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2010, v. 72, n. 4, p. 417, doi. 10.1111/j.1467-9868.2010.00740.x
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- Article
Model-based Boosting 2.0.
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- Journal of Machine Learning Research, 2010, v. 11, n. 8, p. 2109
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- Article
Predicting causal effects in large-scale systems from observational data.
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- Nature Methods, 2010, v. 7, n. 4, p. 247, doi. 10.1038/nmeth0410-247
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- Article
Decomposition and Model Selection for Large Contingency Tables.
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- Biometrical Journal, 2010, v. 52, n. 2, p. 233, doi. 10.1002/bimj.200900083
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- Article
Boosting.
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- WIREs: Computational Statistics, 2010, v. 2, n. 1, p. 69
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- Article
Understanding human functioning using graphical models.
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- BMC Medical Research Methodology, 2010, v. 10, p. 14, doi. 10.1186/1471-2288-10-14
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- Article
Boosting.
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- WIREs: Computational Statistics, 2010, v. 2, p. 69, doi. 10.1002/wics.55
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- Article
Splines for financial volatility.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2009, v. 71, n. 3, p. 655, doi. 10.1111/j.1467-9868.2009.00696.x
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- Article
Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9:131-156, 2008.
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- Journal of Machine Learning Research, 2008, v. 9, n. 2, p. 187
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- Article