Works matching IS 00063444 AND DT 2018 AND VI 105 AND IP 1
Results: 20
On overfitting and post-selection uncertainty assessments.
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- Biometrika, 2018, v. 105, n. 1, p. 221, doi. 10.1093/biomet/asx083
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'On the degrees of freedom of reduced-rank estimators in multivariate regression'.
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- Biometrika, 2018, v. 105, n. 1, p. 247, doi. 10.1093/biomet/asx080
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On bias reduction and incidental parameters.
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- Biometrika, 2018, v. 105, n. 1, p. 233, doi. 10.1093/biomet/asx079
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Shape-constrained partial identification of a population mean under unknown probabilities of sample selection.
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- Biometrika, 2018, v. 105, n. 1, p. 103, doi. 10.1093/biomet/asx077
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Choosing between methods of combining p-values.
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- Biometrika, 2018, v. 105, n. 1, p. 239, doi. 10.1093/biomet/asx076
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Scalar-on-image regression via the soft-thresholded Gaussian process.
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- Biometrika, 2018, v. 105, n. 1, p. 165, doi. 10.1093/biomet/asx075
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Dual regression.
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- Biometrika, 2018, v. 105, n. 1, p. 1, doi. 10.1093/biomet/asx074
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Approximate Bayesian inference under informative sampling.
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- Biometrika, 2018, v. 105, n. 1, p. 91, doi. 10.1093/biomet/asx073
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A structural Markov property for decomposable graph laws that allows control of clique intersections.
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- Biometrika, 2018, v. 105, n. 1, p. 19, doi. 10.1093/biomet/asx072
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Miscellanea: On causal estimation using U-statistics.
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- Biometrika, 2018, v. 105, n. 1, p. 215, doi. 10.1093/biomet/asx071
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Robust and consistent variable selection in high-dimensional generalized linear models.
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- Biometrika, 2018, v. 105, n. 1, p. 31, doi. 10.1093/biomet/asx070
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Kernel-based covariate functional balancing for observational studies.
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- Biometrika, 2018, v. 105, n. 1, p. 199, doi. 10.1093/biomet/asx069
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A conditional composite likelihood ratio test with boundary constraints.
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- Biometrika, 2018, v. 105, n. 1, p. 225, doi. 10.1093/biomet/asx066
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Partial likelihood estimation of isotonic proportional hazards models.
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- Biometrika, 2018, v. 105, n. 1, p. 133, doi. 10.1093/biomet/asx064
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A robust goodness-of-fit test for generalized autoregressive conditional heteroscedastic models.
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- Biometrika, 2018, v. 105, n. 1, p. 73, doi. 10.1093/biomet/asx063
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Simple least squares estimator for treatment effects using propensity score residuals.
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- Biometrika, 2018, v. 105, n. 1, p. 149, doi. 10.1093/biomet/asx062
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Testing for the presence of significant covariates through conditional marginal regression.
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- Biometrika, 2018, v. 105, n. 1, p. 57, doi. 10.1093/biomet/asx061
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Two-sample tests of high-dimensional means for compositional data.
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- Biometrika, 2018, v. 105, n. 1, p. 115, doi. 10.1093/biomet/asx060
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A randomization-based perspective on analysis of variance: a test statistic robust to treatment effect heterogeneity.
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- Biometrika, 2018, v. 105, n. 1, p. 45, doi. 10.1093/biomet/asx059
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Optimal discrimination designs for semiparametric models.
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- Biometrika, 2018, v. 105, n. 1, p. 185, doi. 10.1093/biomet/asx058
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