On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 381, doi. 10.1093/biomet/asz078
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- Publication type:
- Article
Works matching IS 00063444 AND DT 2020 AND VI 107 AND IP 2
Results: 20
1
2
The essential histogram.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 347, doi. 10.1093/biomet/asz081
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- Publication type:
- Article
3
Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 365, doi. 10.1093/biomet/asz083
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- Publication type:
- Article
4
On the marginal likelihood and cross-validation.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 489, doi. 10.1093/biomet/asz077
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- Publication type:
- Article
5
Lassoing eigenvalues.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 397, doi. 10.1093/biomet/asz076
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- Publication type:
- Article
6
Robust empirical Bayes small area estimation with density power divergence.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 467, doi. 10.1093/biomet/asz075
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- Publication type:
- Article
7
A random-perturbation-based rank estimator of the number of factors.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 505, doi. 10.1093/biomet/asz073
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- Publication type:
- Article
8
Estimation of error variance via ridge regression.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 481, doi. 10.1093/biomet/asz074
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- Publication type:
- Article
9
Doubly functional graphical models in high dimensions.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 415, doi. 10.1093/biomet/asz072
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- Publication type:
- Article
10
Adaptive nonparametric regression with the K-nearest neighbour fused lasso.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 293, doi. 10.1093/biomet/asz071
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- Publication type:
- Article
11
Testing conditional mean independence for functional data.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 331, doi. 10.1093/biomet/asz070
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- Article
12
Consistency for the tree bootstrap in respondent-driven sampling.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 497, doi. 10.1093/biomet/asz067
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- Publication type:
- Article
13
Discussion of 'Network cross-validation by edge sampling'.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 281, doi. 10.1093/biomet/asaa022
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- Publication type:
- Article
14
Classification with imperfect training labels.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 311, doi. 10.1093/biomet/asaa011
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- Publication type:
- Article
15
Ensemble estimation and variable selection with semiparametric regression models.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 433, doi. 10.1093/biomet/asaa012
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- Publication type:
- Article
16
Rejoinder: 'Network cross-validation by edge sampling'.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 289, doi. 10.1093/biomet/asaa021
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- Publication type:
- Article
17
Discussion of 'Network cross-validation by edge sampling'.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 277, doi. 10.1093/biomet/asaa017
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- Publication type:
- Article
18
Discussion of 'Network cross-validation by edge sampling'.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 285, doi. 10.1093/biomet/asaa009
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- Publication type:
- Article
19
Network cross-validation by edge sampling.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 257, doi. 10.1093/biomet/asaa006
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- Publication type:
- Article
20
Estimation from cross-sectional data under a semiparametric truncation model.
- Published in:
- Biometrika, 2020, v. 107, n. 2, p. 449, doi. 10.1093/biomet/asaa002
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- Publication type:
- Article