Bayesian compendium.
- Published in:
- Biometrics, 2022, v. 78, n. 2, p. 813, doi. 10.1111/biom.13676
- Publication type:
- Article
Works matching IS 0006341X AND DT 2022 AND VI 78 AND IP 2
Results: 39
1
2
Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders.
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- Biometrics, 2022, v. 78, n. 2, p. 649, doi. 10.1111/biom.13455
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- Article
3
Nonparametric estimation of Spearman's rank correlation with bivariate survival data.
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- Biometrics, 2022, v. 78, n. 2, p. 421, doi. 10.1111/biom.13453
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4
Variance reduction in the inverse probability weighted estimators for the average treatment effect using the propensity score.
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- Biometrics, 2022, v. 78, n. 2, p. 660, doi. 10.1111/biom.13454
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5
Correction to "A penalized framework for distributed lag non‐linear models" by Antonio Gasparrini, Fabian Scheipl, Ben Armstrong, and Michael G. Kenward; 73, 938–948, September 2017.
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- Biometrics, 2022, v. 78, n. 2, p. 812, doi. 10.1111/biom.13682
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- Article
6
Cure models: Methods, applications, and implementation.
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- Biometrics, 2022, v. 78, n. 2, p. 817, doi. 10.1111/biom.13671
- Publication type:
- Article
7
Semiparametric estimation of the nonmixture cure model with auxiliary survival information.
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- Biometrics, 2022, v. 78, n. 2, p. 448, doi. 10.1111/biom.13450
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8
Using R for biostatistics.
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- Biometrics, 2022, v. 78, n. 2, p. 815, doi. 10.1111/biom.13638
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- Article
9
Predictive analytics: Parametric models for regression and classification using R.
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- Biometrics, 2022, v. 78, n. 2, p. 816, doi. 10.1111/biom.13627
- Publication type:
- Article
10
Handbook of meta‐analysis.
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- Biometrics, 2022, v. 78, n. 2, p. 819, doi. 10.1111/biom.13604
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- Article
11
Issue Information.
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- Biometrics, 2022, v. 78, n. 2, p. 417, doi. 10.1111/biom.13489
- Publication type:
- Article
12
Regression‐based negative control of homophily in dyadic peer effect analysis.
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- Biometrics, 2022, v. 78, n. 2, p. 668, doi. 10.1111/biom.13483
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13
On polygenic risk scores for complex traits prediction.
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- Biometrics, 2022, v. 78, n. 2, p. 499, doi. 10.1111/biom.13466
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- Article
14
A Bayesian spatial model for imaging genetics.
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- Biometrics, 2022, v. 78, n. 2, p. 742, doi. 10.1111/biom.13460
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- Article
15
Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring.
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- Biometrics, 2022, v. 78, n. 2, p. 777, doi. 10.1111/biom.13459
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- Publication type:
- Article
16
Sparse linear discriminant analysis for multiview structured data.
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- Biometrics, 2022, v. 78, n. 2, p. 612, doi. 10.1111/biom.13458
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- Article
17
Modeling dynamic correlation in zero‐inflated bivariate count data with applications to single‐cell RNA sequencing data.
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- Biometrics, 2022, v. 78, n. 2, p. 766, doi. 10.1111/biom.13457
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- Publication type:
- Article
18
A generalized robust allele‐based genetic association test.
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- Biometrics, 2022, v. 78, n. 2, p. 487, doi. 10.1111/biom.13456
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- Publication type:
- Article
19
Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data.
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- Biometrics, 2022, v. 78, n. 2, p. 536, doi. 10.1111/biom.13439
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- Article
20
A model for analyzing clustered occurrence data.
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- Biometrics, 2022, v. 78, n. 2, p. 598, doi. 10.1111/biom.13435
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- Publication type:
- Article
21
Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data.
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- Biometrics, 2022, v. 78, n. 2, p. 560, doi. 10.1111/biom.13452
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- Article
22
Cox regression model under dependent truncation.
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- Biometrics, 2022, v. 78, n. 2, p. 460, doi. 10.1111/biom.13451
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- Article
23
Simultaneous estimation of cluster number and feature sparsity in high‐dimensional cluster analysis.
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- Biometrics, 2022, v. 78, n. 2, p. 574, doi. 10.1111/biom.13449
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24
Varying coefficient frailty models with applications in single molecular experiments.
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- Biometrics, 2022, v. 78, n. 2, p. 474, doi. 10.1111/biom.13448
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25
Recruitment prediction for multicenter clinical trials based on a hierarchical Poisson–gamma model: Asymptotic analysis and improved intervals.
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- Biometrics, 2022, v. 78, n. 2, p. 636, doi. 10.1111/biom.13447
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26
Identifying regions of inhomogeneities in spatial processes via an M‐RA and mixture priors.
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- Biometrics, 2022, v. 78, n. 2, p. 798, doi. 10.1111/biom.13446
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27
Geostatistical modeling of positive‐definite matrices: An application to diffusion tensor imaging.
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- Biometrics, 2022, v. 78, n. 2, p. 548, doi. 10.1111/biom.13445
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- Publication type:
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28
Determination and estimation of optimal quarantine duration for infectious diseases with application to data analysis of COVID‐19.
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- Biometrics, 2022, v. 78, n. 2, p. 691, doi. 10.1111/biom.13444
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29
The Tukey trend test: Multiplicity adjustment using multiple marginal models.
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- Biometrics, 2022, v. 78, n. 2, p. 789, doi. 10.1111/biom.13442
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30
EMBRACE: An EM‐based bias reduction approach through Copas‐model estimation for quantifying the evidence of selective publishing in network meta‐analysis.
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- Biometrics, 2022, v. 78, n. 2, p. 754, doi. 10.1111/biom.13441
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31
A kernel regression model for panel count data with nonparametric covariate functions.
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- Biometrics, 2022, v. 78, n. 2, p. 586, doi. 10.1111/biom.13440
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32
Bayesian data fusion: Probabilistic sensitivity analysis for unmeasured confounding using informative priors based on secondary data.
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- Biometrics, 2022, v. 78, n. 2, p. 730, doi. 10.1111/biom.13436
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33
Pursuing sources of heterogeneity in modeling clustered population.
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- Biometrics, 2022, v. 78, n. 2, p. 716, doi. 10.1111/biom.13434
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34
Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach.
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- Biometrics, 2022, v. 78, n. 2, p. 679, doi. 10.1111/biom.13429
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- Article
35
Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data.
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- Biometrics, 2022, v. 78, n. 2, p. 624, doi. 10.1111/biom.13432
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36
Information‐incorporated Gaussian graphical model for gene expression data.
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- Biometrics, 2022, v. 78, n. 2, p. 512, doi. 10.1111/biom.13428
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- Publication type:
- Article
37
Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease.
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- Biometrics, 2022, v. 78, n. 2, p. 435, doi. 10.1111/biom.13427
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- Publication type:
- Article
38
Gaussian graphical model‐based heterogeneity analysis via penalized fusion.
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- Biometrics, 2022, v. 78, n. 2, p. 524, doi. 10.1111/biom.13426
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39
Small‐sample inference for cluster‐based outcome‐dependent sampling schemes in resource‐limited settings: Investigating low birthweight in Rwanda.
- Published in:
- Biometrics, 2022, v. 78, n. 2, p. 701, doi. 10.1111/biom.13423
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