Professor Rosalba Radice

Professor of Statistics

Bayes Business School , Faculty of Actuarial Science and Insurance

Contact

About

Overview

Rosalba Radice has been a faculty member at Bayes Business School (formerly Cass) since September 2018. She received her PhD in Statistics from the University of Bath and, following her PhD, worked as a research assistant and research fellow in the Department of Health Services Research and Policy at LSHTM. From 2012 to 2018, she held roles as a Lecturer, Senior Lecturer, and Reader in Statistics in the Department of Economics, Mathematics, and Statistics at Birkbeck.

Her research focuses on advanced statistical modelling, including copula and flexible parametric approaches, distributional regression, and penalized likelihood-based inference, with applications in health, economics, and the social sciences. This work underpins the development of the open-source R package GJRM, which makes these techniques reproducible and user-friendly, and is complemented by the book Copula Additive Distributional Regression Using R (with additonal R code), offering accessible guidance for applied researchers in health economics, epidemiology, and social sciences. The book has also been reviewed in Biometrics (Kneib, 2026).

From 2021 to 2024, Rosalba was the lead PI on a three-year EPSRC grant [EP/T033061/1], worth approximately £500,000, which supported her research and strengthened her research network. She also served as a member of the editorial team of the Journal of the Royal Statistical Society: Series C from 2020 to 2024. More recently, she was awarded, as Co-Investigator, an NIHR grant [NIHR171530] under the Better Methods, Better Research programme (2025–2028), also worth around £500,000.

At Bayes, Rosalba serves as Faculty Research Director and teaches across postgraduate programmes in actuarial, analytics, and investment-related fields, with a focus on research methods, analytics, and R programming.

 

Qualifications

  • PhD in Statistics, University of Bath, United Kingdom, October 2007 - February 2011
  • MSc in Statistics, University College London, United Kingdom, September 2006 - August 2007
  • Laurea in Economics, University of Bologna, Italy, October 1999 - April 2004

Employment

  • Reader in Statistics, Birkbeck, University of London, United Kingdom, October 2017 - August 2018
  • Honorary Reader in Statistics, University College London, United Kingdom, October 2017 - September 2024
  • Senior Lecturer in Statistics, Birkbeck, University of London, United Kingdom, October 2015 - September 2017
  • Honorary Lecturer in Statistics, University College London, United Kingdom, May 2014 - May 2017
  • Lecturer in Statistics, Birkbeck, University of London, United Kingdom, August 2012 - September 2015
  • Research Fellow of Statistics, London School of Hygiene & Tropical Medicine, United Kingdom, October 2011 - July 2012
  • Research Associate of Statistics, London School of Hygiene & Tropical Medicine, United Kingdom, September 2010 - September 2011
  • Research Associate of Statistics, London School of Hygiene & Tropical Medicine, United Kingdom, September 2010 - September 2011

Expertise

Primary topics

  • Econometric & Statistical Methods
  • Econometrics
  • Statistics

Publications

Book

  1. Marra, G. and Radice, R. (2025). Copula Additive Distributional Regression Using R. ISBN 9781032973111.

Chapter

  1. Klein, N., Kneib, T., Marra, G. and Radice, R. (2020). Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition. Flexible Bayesian Regression Modelling (pp. 121-152). Elsevier. ISBN 9780128158623.

Journal articles (58)

  1. Marra, G. and Radice, R. (2026). Joint modelling of in-hospital mortality and length of stay: a copula additive distributional regression analysis of COVID-19 patient data. Journal of the Royal Statistical Society Series A: Statistics in Society. doi:10.1093/jrsssa/qnag070

    [publisher’s website]

  2. Marra, G. and Radice, R. (2026). Modelling physician visit frequency and costs using a copula additive distributional regression approach. Journal of the Royal Statistical Society Series C: Applied Statistics, 75(2), pp. 431-447. doi:10.1093/jrsssc/qlaf050

    [publisher’s website]

  3. Marra, G. and Radice, R. (2026). Joint Modeling of Birth Outcomes Using a Copula Distributional Regression Approach. Health Economics, 35(3), pp. 399-408. doi:10.1002/hec.70067

    [publisher’s website]

  4. Marra, G. and Radice, R. (2026). Bivariate Copula‐Based Regression for Joint Modeling of Healthcare Visits. Health Economics, 35(2), pp. 332-345. doi:10.1002/hec.70059

    [publisher’s website]

  5. Dorn, F., Radice, R., Marra, G. and Kneib, T. (2024). A bivariate relative poverty line for leisure time and income poverty: Detecting intersectional differences using distributional copulas. Review of Income and Wealth, 70(2), pp. 395-419. doi:10.1111/roiw.12635

    [publisher’s website]

  6. Marra, G., Radice, R. and Zimmer, D. (2024). A Unifying Switching Regime Regression Framework with Applications in Health Economics. Econometric Reviews. doi:10.1080/07474938.2023.2255438
  7. Cust, H., Lépine, A., Treibich, C., Powell‐Jackson, T., Radice, R. and Tidiane Ndour, C. (2024). Trading HIV for sheep: Risky sexual behavior and the response of female sex workers to Tabaski in Senegal. Health Economics, 33(1), pp. 153-193. doi:10.1002/hec.4756

    [publisher’s website]

  8. Filippou, P., Marra, G., Radice, R. and Zimmer, D. (2023). Estimating the Impact of Medical Care Usage on Work Absenteeism by a Trivariate Probit Model with Two Binary Endogenous Explanatory Variables. AStA Advances in Statistical Analysis, 107(4), pp. 713-731. doi:10.1007/s10182-022-00464-6

    [publisher’s website]

  9. Eletti, A., Marra, G. and Radice, R. (2023). A spline-based framework for the flexible modelling of continuously observed multistate survival processes. Statistical Modelling, 23(5-6), pp. 495-509. doi:10.1177/1471082x231176120

    [publisher’s website]

  10. Marra, G., Fasiolo, M., Radice, R. and Winkelmann, R. (2023). A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health. Health Economics, 32(6), pp. 1305-1322. doi:10.1002/hec.4668

    [publisher’s website]

  11. Petti, D., Eletti, A., Marra, G. and Radice, R. (2022). Copula link-based additive models for bivariate time-to-event outcomes with general censoring scheme. Computational Statistics & Data Analysis, 175, pp. 107550-107550. doi:10.1016/j.csda.2022.107550

    [publisher’s website]

  12. Ranjbar, S., Cantoni, E., Chavez-Demoulin, V., Marra, G., Radice, R. and Jaton, K. (2022). Modelling the Extremes of Seasonal Viruses and Hospital Congestion: The Example of Flu in a Swiss Hospital. Journal of the Royal Statistical Society Series C: Applied Statistics, 71(4), pp. 884-905. doi:10.1111/rssc.12559

    [publisher’s website]

  13. Eletti, A., Marra, G., Quaresma, M., Radice, R. and Rubio, F.J. (2022). A Unifying Framework for Flexible Excess Hazard Modelling with Applications in Cancer Epidemiology. Journal of the Royal Statistical Society Series C: Applied Statistics, 71(4), pp. 1044-1062. doi:10.1111/rssc.12566

    [publisher’s website]

  14. Marra, G., Radice, R. and Zimmer, D. (2021). Did the ACA's “guaranteed issue” provision cause adverse selection into nongroup insurance? Analysis using a copula‐based hurdle model. Health Economics, 30(9), pp. 2246-2263. doi:10.1002/hec.4372

    [publisher’s website]

  15. Cust, H., Jones, H., Powell-Jackson, T., Lépine, A. and Radice, R. (2021). Economic shocks and risky sexual behaviours in low- and middle-income countries: a systematic review of the literature. Journal of Development Effectiveness, 13(2), pp. 166-203. doi:10.1080/19439342.2021.1928734

    [publisher’s website]

  16. Marra, G., Farcomeni, A. and Radice, R. (2021). Link-based survival additive models under mixed censoring to assess risks of hospital-acquired infections. Computational Statistics & Data Analysis, 155, pp. 107092-107092. doi:10.1016/j.csda.2020.107092

    [publisher’s website]

  17. Aeberhard, W.H., Cantoni, E., Marra, G. and Radice, R. (2021). Robust fitting for generalized additive models for location, scale and shape. Statistics and Computing, 31(1). doi:10.1007/s11222-020-09979-x

    [publisher’s website]

  18. van der Wurp, H., Groll, A., Kneib, T., Marra, G. and Radice, R. (2020). Generalised joint regression for count data: a penalty extension for competitive settings. Statistics and Computing, 30(5), pp. 1419-1432. doi:10.1007/s11222-020-09953-7

    [publisher’s website]

  19. Marra, G., Radice, R. and Zimmer, D.M. (2020). Estimating The Binary Endogenous Effect of Insurance on Doctor Visits by Copula-Based Regression Additive Models. Journal of the Royal Statistical Society Series C: Applied Statistics, 69(4), pp. 953-971. doi:10.1111/rssc.12419

    [publisher’s website]

  20. Dettoni, R., Marra, G. and Radice, R. (2020). Generalized Link-Based Additive Survival Models with Informative Censoring. Journal of Computational and Graphical Statistics, 29(3), pp. 503-512. doi:10.1080/10618600.2020.1724544

    [publisher’s website]

  21. Marra, G. and Radice, R. (2020). Copula Link-Based Additive Models for Right-Censored Event Time Data. Journal of the American Statistical Association, 115(530), pp. 886-895. doi:10.1080/01621459.2019.1593178

    [publisher’s website]

  22. van der Wurp, H., Groll, A., Kneib, T., Marra, G. and Radice, R. (2020). Generalised joint regression for count data with a focus on modelling football matches. Statistics and Computing, 30(5), pp. 1419-1432. doi:10.1007/s11222-020-09953-7

    [publisher’s website]

  23. Espasandín‐Domínguez, J., Cadarso‐Suárez, C., Kneib, T., Marra, G., Klein, N., Radice, R.... Gude, F. (2019). Assessing the relationship between markers of glycemic control through flexible copula regression models. Statistics in Medicine, 38(27), pp. 5161-5181. doi:10.1002/sim.8358

    [publisher’s website]

  24. Klein, N., Kneib, T., Marra, G., Radice, R., Rokicki, S. and McGovern, M.E. (2019). Mixed binary‐continuous copula regression models with application to adverse birth outcomes. Statistics in Medicine, 38(3), pp. 413-436. doi:10.1002/sim.7985

    [publisher’s website]

  25. Gomes, M., Radice, R., Camarena Brenes, J. and Marra, G. (2019). Copula selection models for non‐Gaussian outcomes that are missing not at random. Statistics in Medicine, 38(3), pp. 480-496. doi:10.1002/sim.7988

    [publisher’s website]

  26. Wojtys, M., Marra, G. and Radice, R. (2018). Copula based generalized additive models for location, scale and shape with non-random sample selection. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 127, pp. 1-14. doi:10.1016/j.csda.2018.05.001

    [publisher’s website]

  27. Filippou, P., Kneib, T., Marra, G. and Radice, R. (2018). A trivariate additive regression model with arbitrary link functions and varying correlation matrix. Journal of Statistical Planning and Inference, 199, pp. 236-248. doi:10.1016/j.jspi.2018.07.002

    [publisher’s website]

  28. Braumoeller, B.F., Marra, G., Radice, R. and Bradshaw, A.E. (2018). Flexible Causal Inference for Political Science. POLITICAL ANALYSIS, 26(1), pp. 54-71. doi:10.1017/pan.2017.29

    [publisher’s website]

  29. Marra, G. and Radice, R. (2017). A joint regression modeling framework for analyzing bivariate binary data in R. Dependence Modeling, 5(1), pp. 268-294. doi:10.1515/demo-2017-0016

    [publisher’s website]

  30. Marra, G. and Radice, R. (2017). Bivariate copula additive models for location, scale and shape. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 112, pp. 99-113. doi:10.1016/j.csda.2017.03.004

    [publisher’s website]

  31. Filippou, P., Marra, G. and Radice, R. (2017). Penalized likelihood estimation of a trivariate additive probit model. Biostatistics, 18(3), pp. 569-585. doi:10.1093/biostatistics/kxx008

    [publisher’s website]

  32. Marra, G., Radice, R., Bärnighausen, T., Wood, S.N. and McGovern, M.E. (2017). A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses. Journal of the American Statistical Association, 112(518), pp. 484-496. doi:10.1080/01621459.2016.1224713

    [publisher’s website]

  33. Marra, G., Radice, R. and Filippou, P. (2017). Regression spline bivariate probit models: A practical approach to testing for exogeneity. Communications in Statistics - Simulation and Computation, 46(3), pp. 2283-2298. doi:10.1080/03610918.2015.1041974

    [publisher’s website]

  34. Kreif, N., Gruber, S., Radice, R., Grieve, R. and Sekhon, J.S. (2016). Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching. Statistical Methods in Medical Research, 25(5), pp. 2315-2336. doi:10.1177/0962280214521341

    [publisher’s website]

  35. Radice, R., Marra, G. and Wojtyś, M. (2016). Copula regression spline models for binary outcomes. Statistics and Computing, 26(5), pp. 981-995. doi:10.1007/s11222-015-9581-6

    [publisher’s website]

  36. Wojtys, M., Marra, G. and Radice, R. (2016). Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel. JOURNAL OF STATISTICAL SOFTWARE, 71(6), pp. 1-66. doi:10.18637/jss.v071.i06

    [publisher’s website]

  37. McGovern, M.E., Bärnighausen, T., Marra, G. and Radice, R. (2015). On the Assumption of Bivariate Normality in Selection Models. Epidemiology, 26(2), pp. 229-237. doi:10.1097/ede.0000000000000218

    [publisher’s website]

  38. McGovern, M.E., Marra, G., Radice, R., Canning, D., Newell, M. and Bärnighausen, T. (2015). Adjusting HIV prevalence estimates for non‐participation: an application to demographic surveillance. Journal of the International AIDS Society, 18(1). doi:10.7448/ias.18.1.19954

    [publisher’s website]

  39. Zanin, L., Radice, R. and Marra, G. (2015). Modelling the impact of women's education on fertility in Malawi. JOURNAL OF POPULATION ECONOMICS, 28(1), pp. 89-111. doi:10.1007/s00148-013-0502-8

    [publisher’s website]

  40. Marra, G. and Radice, R. (2014). Flexible Bivariate Binary Models for Estimating the Efficacy of Phototherapy for Newborns with Jaundice. International Journal of Statistics and Probability, 4(1). doi:10.5539/ijsp.v4n1p46

    [publisher’s website]

  41. Zanin, L., Radice, R. and Marra, G. (2014). A comparison of approaches for estimating the effect of women's education on the probability of using modern contraceptive methods in Malawi. The Social Science Journal, 51(3), pp. 361-367. doi:10.1016/j.soscij.2013.12.008

    [publisher’s website]

  42. Marra, G., Radice, R. and Missiroli, S. (2014). Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models. Computational Statistics, 29(3-4), pp. 715-741. doi:10.1007/s00180-013-0458-x

    [publisher’s website]

  43. Ieva, F., Marra, G., Paganoni, A.M. and Radice, R. (2014). A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients. Computational and Mathematical Methods in Medicine, 2014, pp. 1-7. doi:10.1155/2014/240435

    [publisher’s website]

  44. Kreif, N., Grieve, R., Radice, R. and Sekhon, J.S. (2013). Regression-adjusted matching and double-robust methods for estimating average treatment effects in health economic evaluation. Health Services and Outcomes Research Methodology, 13(2-4), pp. 174-202. doi:10.1007/s10742-013-0109-2

    [publisher’s website]

  45. Radice, R., Zanin, L. and Marra, G. (2013). On the effect of obesity on employment in the presence of observed and unobserved confounding. Statistica Neerlandica, 67(4), pp. 436-455. doi:10.1111/stan.12016

    [publisher’s website]

  46. Zanin, L., Radice, R. and Marra, G. (2013). Estimating the Effect of Perceived Risk of Crime on Social Trust in the Presence of Endogeneity Bias. Social Indicators Research, 114(2), pp. 523-547. doi:10.1007/s11205-012-0160-3

    [publisher’s website]

  47. Marra, G., Papageorgiou, G. and Radice, R. (2013). Estimation of a Semiparametric Recursive Bivariate Probit Model with Nonparametric Mixing. Australian and New Zealand Journal of Statistics, 55(3), pp. 321-342. doi:10.1111/anzs.12043

    [publisher’s website]

  48. Marra, G. and Radice, R. (2013). Estimation of a regression spline sample selection model. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 61, pp. 158-173. doi:10.1016/j.csda.2012.12.010

    [publisher’s website]

  49. Marra, G. and Radice, R. (2013). A penalized likelihood estimation approach to semiparametric sample selection binary response modeling. Electronic Journal of Statistics, 7(none). doi:10.1214/13-ejs814

    [publisher’s website]

  50. Sobotka, F., Radice, R., Marra, G. and Kneib, T. (2013). Estimating the Relationship Between Women's Education and Fertility in Botswana by Using an Instrumental Variable Approach to Semiparametric Expectile Regression. Journal of the Royal Statistical Society Series C: Applied Statistics, 62(1), pp. 25-45. doi:10.1111/j.1467-9876.2012.01050.x

    [publisher’s website]

  51. Radice, R. (2012). A Bayesian Approach to Modelling Reticulation Events with Application to the Ribosomal Protein Generps11of Flowering Plants. Australian & New Zealand Journal of Statistics, 54(4), pp. 401-426. doi:10.1111/j.1467-842x.2012.00686.x

    [publisher’s website]

  52. Kreif, N., Grieve, R., Radice, R., Sadique, Z., Ramsahai, R. and Sekhon, J.S. (2012). Methods for Estimating Subgroup Effects in Cost-Effectiveness Analyses That Use Observational Data. Medical Decision Making, 32(6), pp. 750-763. doi:10.1177/0272989x12448929

    [publisher’s website]

  53. Radice, R., Ramsahai, R., Grieve, R., Kreif, N., Sadique, Z. and Sekhon, J.S. (2012). Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach. The International Journal of Biostatistics, 8(1). doi:10.1515/1557-4679.1382

    [publisher’s website]

  54. Marra, G. and Radice, R. (2011). A flexible instrumental variable approach. Statistical Modelling, 11(6), pp. 581-603. doi:10.1177/1471082x1001100607

    [publisher’s website]

  55. Marra, G. and Radice, R. (2011). Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity. Canadian Journal of Statistics, 39(2), pp. 259-279. doi:10.1002/cjs.10100

    [publisher’s website]

  56. Marra, G. and Radice, R. (2011). Do We Adequately Control for Unmeasured Confounders When Estimating the Short-term Effect of Air Pollution on Mortality? Water, Air, & Soil Pollution, 218(1-4), pp. 347-352. doi:10.1007/s11270-010-0648-x

    [publisher’s website]

  57. Marra, G. and Radice, R. (2010). Penalised regression splines: theory and application to medical research. Statistical Methods in Medical Research, 19(2), pp. 107-125. doi:10.1177/0962280208096688

    [publisher’s website]

  58. Monfardini, C. and Radice, R. (2008). Testing Exogeneity in the Bivariate Probit Model: A Monte Carlo Study*. Oxford Bulletin of Economics and Statistics, 70(2), pp. 271-282. doi:10.1111/j.1468-0084.2007.00486.x

    [publisher’s website]

Scholarly edition

  1. Fabbri, D., Monfardini, C. and Radice, R. (2004). Testing exogeneity in the bivariate probit model: Monte Carlo evidence and an application to health economics.

Working paper

  1. Marra, G., Radice, R. and Zimmer, D. (2024). A unifying switching regime regression framework with applications in health economics. Informa UK Limited.