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Impact of nonignorable coarsening on Bayesian inference
Jiameng Zhang,
Daniel F. Heitjan
Clinical Sciences
Clinical Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
22
Scopus citations
Overview
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Dive into the research topics of 'Impact of nonignorable coarsening on Bayesian inference'. Together they form a unique fingerprint.
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Mathematics
Coarsening
83%
Bayesian inference
69%
Data Model
49%
Maximum Likelihood Estimate
20%
Ignorability
17%
Bayesian Modeling
14%
Leukemia
14%
Incompleteness
13%
Derivative
13%
Grouping
12%
Bayesian Analysis
11%
Missing Data
11%
Censoring
11%
Model
10%
Count
9%
Robustness
9%
Evaluate
8%
Cover
7%
Range of data
6%
Generalise
5%
Form
4%
Business & Economics
Bayesian Inference
94%
Maximum Likelihood
26%
Derivatives
23%
Bayesian Modeling
21%
Bayesian Analysis
20%
Censoring
18%
Relapse
17%
Missing Data
16%
Robustness
16%
Incompleteness
15%
Grouping
12%
Inference
10%
Medicine & Life Sciences
Likelihood Functions
100%
Bayes Theorem
39%
CD4 Lymphocyte Count
33%
Leukemia
26%
HIV
22%
Recurrence
18%