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Copula additive distributional regression enables the joint modeling of multiple outcomes, an essential aspect of many real-world research problems. This book provides an accessible overview of this modeling approach, with a particular focus on its implementation in the GJRM R package
List of contents
1. Core concepts in copula regression. 2. Continuous outcomes. 3. Count outcomes . 4. Survival outcomes. 5. Binary outcomes . 6. Ordinal outcomes. 7. Binary outcome with partial observability. 8. Ordinal and continuous outcomes. 9. Binary and continuous outcomes. 10. Binary and count outcomes. 11. Count and continuous outcomes. 12. Binary outcome with binary treatment effect. 13. Time-to-event outcome with binary treatment effect. 14. Binary outcome with missingness not at random.
About the author
Giampiero Marra is a Professor of Statistics in the Department of Statistical Science at University College London (UCL). He holds a degree in Statistics and Economics from the University of Bologna (2004) and began his career in consultancy roles in the private sector. In 2007, he completed an MSc in Statistics at UCL and successfully defended his PhD thesis at the University of Bath in November 2010. Giampiero joined UCL as a faculty member in September 2010.
Rosalba Radice is a Professor of Statistics at Bayes Business School, City, University of London. After earning her PhD in Statistics from the University of Bath, she held positions as a research assistant and research fellow at the London School of Hygiene and Tropical Medicine. From 2012 to 2018, Rosalba served as Lecturer, Senior Lecturer and then Reader in Statistics at Birkbeck, University of London.
For over 15 years, Giampiero and Rosalba have collaborated extensively to advance methodological, computational and applied statistics. Their research spans diverse areas, including penalized likelihood-based inference, copula regression and survival analysis, with impactful applications in fields such as healthcare, economics, epidemiology and the social sciences. As part of their work, they developed the GJRM package for R, which enables researchers and practitioners to implement these methods effectively while promoting transparency and reproducibility.
