Professor Rosalba Radice

Professor of Statistics

Bayes Business School, Faculty of Actuarial Science and Insurance

Contact

Visit Rosalba Radice

Room BR4086, Bunhill Row

Postal address

Bayes Business School
106 Bunhill Row
London
EC1Y 8TZ
United Kingdom

About

Overview

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

Her main research interests are in the areas of statistical modelling, copula regression, flexible parametric modelling, distributional regression, penalized likelihood based inference with applications in epidemiology and economics.

Recently Rosalba has been awarded a three year EPSRC grant as lead-PI of more than £500,000 to support her research and solidify her research network. She is also in the editorial team of the Journal of the Royal Statistical Society Series C.

At Bayes she teaches students on the modules Analytics Methods for Business for the MSc Business Analytics and Research Methods for Actuarial Professionals for the MSc Actuarial Science.

Qualifications

  • PhD in Statistics, University of Bath, United Kingdom, Oct 2007 – Feb 2011
  • MSc in Statistics, University College London, United Kingdom, Sep 2006 – Aug 2007
  • Laurea in Economics, University of Bologna, Italy, Oct 1999 – Apr 2004

Employment

  • Reader in Statistics, Birkbeck, University of London, Oct 2017 – Aug 2018
  • Honorary Reader in Statistics, University College London, Oct 2017 – present
  • Senior Lecturer in Statistics, Birkbeck, University of London, Oct 2015 – Sep 2017
  • Honorary Lecturer in Statistics, University College London, May 2014 – May 2017
  • Lecturer in Statistics, Birkbeck, University of London, Aug 2012 – Sep 2015
  • Research Fellow of Statistics, London School of Hygiene & Tropical Medicine, Oct 2011 – Jul 2012
  • Research Associate of Statistics, London School of Hygiene & Tropical Medicine, Sep 2010 – Sep 2011

Expertise

Primary topics

  • Econometric & Statistical Methods
  • Econometrics
  • Statistics

Publications

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.

Journal articles (52)

  1. 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]

  2. Eletti, A., Marra, G. and Radice, R. (2023). A Spline-Based Framework for the Flexible Modelling of Continuously Observed Multistate Survival Processes. Statistical Modelling: An International Journal.
  3. Dorn, F., Radice, R., Marra, G. and Kneib, T. (2023). A bivariate relative poverty line for leisure time and income poverty: Detecting intersectional differences using distributional copulas. Review of Income and Wealth. doi:10.1111/roiw.12635.

    [publisher’s website]

  4. 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]

  5. Filippou, P., Marra, G., Radice, R. and Zimmer, D. (2022). 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. doi:10.1007/s10182-022-00464-6.

    [publisher’s website]

  6. 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]

  7. 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]

  8. 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]

  9. 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]

  10. 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]

  11. 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]

  12. 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, pp. 1419–1432. doi:10.1007/s11222-020-09953-7.
  13. 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]

  14. 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]

  15. 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]

  16. 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]

  17. 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]

  18. 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]

  19. 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]

  20. 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.
  21. 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.
  22. 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.
  23. 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]

  24. 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.
  25. 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]

  26. 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]

  27. 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.
  28. 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.
  29. 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]

  30. 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.
  31. 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]

  32. McGovern, M.E., Marra, G., Radice, R., Canning, D., Newell, M.-.L. and Bärnighausen, T. (2015). Adjusting HIV prevalence estimates for non-participation: an application to demographic surveillance. Journal of the International AIDS Society, 18(1), pp. 19954–19954. doi:10.7448/ias.18.1.19954.

    [publisher’s website]

  33. 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.
  34. 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]

  35. 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]

  36. 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]

  37. 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.
  38. 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]

  39. 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]

  40. 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]

  41. 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.
  42. 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.
  43. 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]

  44. 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]

  45. 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]

  46. 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]

  47. 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]

  48. 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]

  49. 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]

  50. 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]

  51. 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]

  52. 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