Giampiero Marra - Professor of Statistics

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Giampiero Department of Statistical Science
University College London
Gower Street
London WC1E 6BT

Office: Room 142
1-19 Torrington Place
London WC1E 7HB

Phone: +44 (0) 20 7679 1864
Fax:     +44 (0) 20 3108 3105
E-mail: giampiero.marra -AT- ucl.ac.uk

Profile

I work as a Professor of Statistics at the Department of Statistical Science at University College London. After having graduated in Statistics and Economics at the University of Bologna in 2004, I worked as an econometrician and statistician for a consulting firm and a multinational company. In 2007 I was awarded an MSc in Statistics at UCL and I defended my PhD thesis at the University of Bath in November 2010. I joined UCL in September 2010.

Main research interests

Penalized likelihood based inference in semiparametric simultaneous joint equation models, copula regression, generalized additive modelling, distributional regression, generalised additive models for location, scale and shape, flexible survival modelling. Keywords: endogeneity, non-random sample selection, MNAR missing data, unobserved confounding, penalised regression spline, copula, generalized regression, joint models, computational statistics, gamlss, survival data.

As highlighted by my published papers, I engage considerably in cross-disciplinary work in several areas such as probabilistic risk assessment, transport studies, political science, HIV and cancer research, credit risk, crime and perceived social trust. I am a member of several research groups at UCL.

I am interested in taking on PhD students working on methodological and applied topics (see my publication list to get a feel about my work). Feel free to get in touch with me and include a brief CV and research plan (if any).

A list of published papers is here

Copula Additive Distributional Regression Using R

Copula Additive Distributional Regression Using R

Additional R code

Book review: Biometrics, 82(2), June 2026

Editorial service

Associate Editor of Statistics and Computing, 2015 - 2022

Member of Editorial Advisory Board of Dependence Modeling, 2018 -

Associate Editor of Journal of the Royal Statistical Society: Series C, 2018 - 2021

Associate Editor of Statistical Methods & Applications, 2019 - 2023

Associate Editor of AStA Advances in Statistical Analysis, 2020 -

GJRM package R package

For more than 16 years, we have been extending, maintaining and developing the freely available GJRM R package, which implements the algorithms behind all the methods and models introduced in my research papers. In modern scientific practice, software is essential: it enables transparent and reproducible research, accelerates methodological dissemination, and bridges the gap between theory and application.

GJRM provides a unified framework for fitting a wide range of joint regression models with flexible covariate effects (e.g., linear, non-linear, spatial) and diverse response types (count, binary, continuous, ordinal, survival). It supports many modelling scenarios, including associated responses and non-random sample selection. All model parameters can be expressed through flexible additive predictors, giving users substantial modelling freedom. Originally designed for semiparametric bivariate probit models (hence the earlier package name SemiParBIVProbit), GJRM has expanded enormously and now supports a broad collection of models, including:

  • Univariate GAMLSS / distributional regression models, including distributions for extreme values.
  • Univariate and bivariate copula survival models, with or without informative censoring, excess hazard, and all standard censoring mechanisms.
  • Copula models with continuous, count, binary, ordinal or survival margins in the presence of associated responses.
  • Trivariate binary models with non-random sample selection or associated responses.
  • Trivariate PCC models with continuous margins.

    • New models and options are added regularly. Several methods in the package were developed following specific requests from researchers and collaborators. If you are interested in a particular feature, would like to discuss potential developments or data analyses, or simply want to share how you use GJRM in academia or industry, feel free to get in touch.

    Selected invited talks/seminars/workshops/short courses

    10/2026: Copula Additive Distributional Regression Using R, Training School (Learning from Complex Data: Statistical and AI Perspectives), University of Calabria, Italy
    10/2024: Copula Additive Distributional Regression (for Ordinal Outcomes) in R, Challenges for Categorical Data Analysis, LSE, UK
    10/2024: Copula Additive Distributional Regression, Methods for Fintech and Artificial Intelligence in Finance, Naples, Italy
    06/2023: Workshop on Sample Selection Modelling for Missingness Not at Random using GJRM (Bayes Business School, City, University of London)
    06/2023: A Unifying and Flexible Copula Regression Modelling Framework (RSS South West Seminar, University of Plymouth)
    02/2020: Estimating HIV Prevalence in the Presence of Missingness not at Random by Copula-Based Regression Additive Models (Warwick Medical School, UK)
    01/2020: Estimating the Effect of Insurance on Doctor Visits by Copula-Based Regression Additive Models (School of Mathematics, Edinburgh)
    05/2019: Generalised Joint Regression Modelling with Application to the Effect of Insurance on Doctor Visits (Cass Business School, London)
    03/2019: Generalised Joint Regression Modelling (Speaker, DAGStat 2019, Munich)
    12/2018: Copula additive regression models with endogenous binary treatment and count response (CFE-ERCIM 2018, University of Pisa)
    12/2017: Joint Generalized Survival Models (CFE-ERCIM 2017, University of London)
    07/2017: A new approach to fitting generalised additive models for location, scale and shape (EMS, Helsinki)
    07/2017: Semiparametric Copula-Based Regression Models (Speaker, 38th Annual Conference of the International Society for Clinical Biostatistics, Vigo, Spain)
    05/2017: A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses (University of Southampton, CORMSIS Centre)
    02/2017: A Unified Approach to Estimating Bivariate Copula Additive Models (Technische Universitat Munchen, Germany)
    01/2017: One-Day Short Course on Copula Generalised Additive Models for Location, Scale and Shape with R (3rd BIOSTATNET General Meeting, Santiago de Compostela, Spain)
    10/2016: On the Specification and Estimation of Bivariate Copula-Based Regression Models (Research Center for Statistics - UNIGE, Geneva, Switzerland)
    08/2016: Bivariate Copula Additive Models for Location, Scale and Shape (COMPSTAT 2016, Oviedo, Spain)
    09/2016: Two-Day Workshop on Missing Data with Emphasis on Sample Selection Models (Birkbeck, University of London)
    06/2016: One-Day Workshop on Bivariate Copula Additive Models for Location, Scale and Shape (Barclays Investment Bank, London)
    05/2016: One-Day Workshop on Bivariate Copula Additive Models for Location, Scale and Shape (Ministry of Justice, London)
    01/2016: A Copula-Based Regression Framework to Deal with Non-Compliance in RCTs (UCL Medical School, Royal Free Hospital, UK)
    01/2016: Flexible Bivariate Copula-Based Regression (University of Durham, UK)
    08/2015: Workshops on Missing Data using Selection Models (Speaker, CDC in Atlanta and Department of Global Health and Population, Harvard)
    08/2015: A Unified Modeling Approach to Estimating HIV Prevalence in Sub-Saharan African Countries (Georg-August-Universitat Gottingen, Germany)
    12/2014: Copula Regression Spline Models for Binary Outcomes (CFE-ERCIM 2014, University of Pisa, Italy)
    11/2014: Statistical Analysis in Development Economics (Speaker, Erasmus School of Economics, Rotterdam)
    02/2014: Flexible Binary Response Sample Selection Models with Application to HIV Prevalence Estimation (Imperial College London and MRC Clinical Trials Unit London)
    06/2013: Binary Generalized Extreme Value Additive Modelling and Beyond (CFE-ERCIM 2013, University of London)
    12/2012: Practical Variable Selection for Generalized Models (Computing and Statistics ERCIM 2012, Oviedo, Spain)
    02/2012: Modelling Correlated Binary Responses within a Semiparametric Regression Framework (University of Birmingham)

    PDRA and PhD UCL students

    Alessia Eletti, PhD project on multi-state and survival modelling, 2020 - 2024
    Malgorzata Wojtys (PDRA), worked on an EPSRC funded project on semiparametric sample selection models, 2012 - 2013
    Ken Liang, PhD project on nonparametric spatio-temporal quantile regression, 2010 - 2014
    Francesco Donat, PhD project on bivariate response ordered probit modelling, 2012 - 2015
    Panagiota Filippou, EPSRC PhD funded project on joint modelling of endogeneity and non-random sample selection, 2013 - 2017
    Karol Wyszynski, PhD project on sample selection models for count data, 2012 - 2015
    Robson Jose Mariano Machado, PhD project on multistate models, 2014 - 2018
    Robinson Dettoni Hidalgo, PhD sponsored by Chilean Government, 2016 - 2020
    Maria Toomik, PhD sponsored by A*STAR, Statistical analysis for highly structured data, 2014 - 2019
    Valentina Marincioni, PhD, Bartlett School of Environment (Energy and Resources), 2014 - 2019

    Teaching and admin duties

    2024-
    Postgraduate admission tutor

    2016-2021
    Undergraduate admission tutor

    Since 2011/2012
    STAT0028: Statistical Models and Data Analysis (1st Term)

    2012/2013
    STATG003: Statistical Computing (shared, 1st Term)

    2011/2012
    MATH7501: Probability and Statistics (2nd Term)

    2010/2011
    STAT2001/3101/D2: Probability and Inference (tutorials, 1st Term)
    MATH7501: Probability and Statistics (2nd Term)


    Employment and Education history

    Last modified: June 2026