Imputation and post-selection inference in models with missing data: an application to colorectal cancer surveillance guidelines
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Publication:2281190
DOI10.1214/19-AOAS1239zbMath1434.62226OpenAlexW2980972558MaRDI QIDQ2281190
Karen Messer, Loki Natarajan, Yuqi Qiu, Lin Liu
Publication date: 19 December 2019
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.aoas/1571277757
Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Paired and multiple comparisons; multiple testing (62J15) Missing data (62D10)
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- Model selection and model averaging after multiple imputation
- Imputation and post-selection inference in models with missing data: an application to colorectal cancer surveillance guidelines
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- Model Selection and Model Averaging
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- MINIMIZING AVERAGE RISK IN REGRESSION MODELS
- Asymptotic Statistics
- Frequentist Model Average Estimators
- The Focused Information Criterion
- Stability Selection
- Variable selection when missing values are present: a case study
- Estimation and Accuracy After Model Selection
- Comment
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Measurement Error in Nonlinear Models
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