A Note on the Large Sample Properties of Estimators Based on Generalized Linear Models for Correlated Pseudo‐observations
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Publication:2821483
DOI10.1111/SJOS.12212zbMath1468.62323OpenAlexW2285445179MaRDI QIDQ2821483
Martin Jacobsen, Torben Martinussen
Publication date: 21 September 2016
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/sjos.12212
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Cites Work
- Unnamed Item
- On pseudo-values for regression analysis in competing risks models
- A study of a class of weighted bootstrap for censored data
- Dynamic regression models for survival data.
- Approximation Theorems of Mathematical Statistics
- Generalised linear models for correlated pseudo-observations, with applications to multi-state models
- Statistical models based on counting processes
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