Predictive Cross-validation for the Choice of Linear Mixed-Effects Models with Application to Data from the Swiss HIV Cohort Study
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Publication:2893982
DOI10.1111/j.1541-0420.2011.01621.xzbMath1241.62149OpenAlexW2076024536WikidataQ33952896 ScholiaQ33952896MaRDI QIDQ2893982
Leonhard Held, Julia Braun, Bruno Ledergerber
Publication date: 27 June 2012
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/j.1541-0420.2011.01621.x
Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)
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Cites Work
- Unnamed Item
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- Random-Effects Models for Longitudinal Data
- Parametric and nonparametric Bayesian model specification: a case study involving models for count data
- Scoring rules and the evaluation of probabilities. (With discussion)
- Selecting mixed-effects models based on a generalized information criterion
- Counting degrees of freedom in hierarchical and other richly-parameterised models
- On the behaviour of marginal and conditional AIC in linear mixed models
- A Score Regression Approach to Assess Calibration of Continuous Probabilistic Predictions
- A note on conditional AIC for linear mixed-effects models
- Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach
- Model Selection and Model Averaging
- Model Selection for Linear Mixed Models Using Predictive Criteria
- Prediction and Estimation of Growth Curves With Special Covariance Structures
- The Schwarz criterion and related methods for normal linear models
- A Predictive Approach to Model Selection
- Bayes Factors and Approximations for Variance Component Models
- ON CROSS‐VALIDATION FOR SMOOTHING SPLINES IN THE CASE OF DEPENDENT OBSERVATIONS
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Probabilistic Forecasts, Calibration and Sharpness
- Conditional Akaike information for mixed-effects models