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Imperfect Data : From Mathematical Foundations to Applications in Life Sciences – IMAL

ARC 20/25-107 (2020-2025)

Joint UCLouvain-UNamur ARC project / Sponsored by the Wallonia-Brussels Federation

Promotors:
•    Catherine Legrand (UCLouvain, spoke-person)
•    Anouar El Ghouch (UCLouvain)
•    Philippe Lambert (ULiege / UCLouvain)
•    Eugen Pircalabelu (UCLouvain)
•    Germain Van Bever (UNamur)
•    Ingrid Van Keilegom (KULeuven / UCLouvain).

Summary:
We are witnessing a period of time where the data collection potential has increased exponentially. The cautionary tale of this big data era is that large amounts of data do not necessarily contribute to an increment in our knowledge about the underlying phenomenon. One of the principal reasons for this is that even though one would desire to measure a characteristic for a subject, in many instances one can only get an approximate measurement due to difficulty in obtaining the direct measurement of the desired phenomenon (e.g. tumor size), non-replicability across instances (e.g. blood pressure), necessity to obtain numerous measurements rapidly, sometimes at the cost of accuracy. As a result, many modern observed markers are proxies for the real data because invasive, costly or too complex methods would be required to obtain accurate measurements. In this project we study how one can correct for different types of imperfect data when building statistical models with a focus on applications coming from life sciences. Imperfect data appear in different contexts, structures and models, and this project focuses on two common settings which regularly suffer from imperfect data: data in a regression context with imperfectly measured explanatory variables (Theme 1) and high-dimensional or functional data with measurement error (Theme 2).

Promotors and research team

Anouar El Ghouch

  • Anouar El Ghouch (anouar.elghouch@uclouvain.be) is Associate Professor of Statistics at UCLouvain and has been working at the Institute of Statistics, Biostatistics and Actuarial Sciences within LIDAM in the Faculty of Science since 2009. Before joining UC Louvain, he held a Postdoc position at the University of Geneva in the Centre for Research in Statistics. He obtained his PhD in Statistics in 2007 from the Institute of Statistics at UCLouvain. His research interests include robust statistics, survival analysis, nonparametric methods and regression analysis. He was associate editor of Computational Statistics & Data Analysis (2018-2021).
  • Google Scholar: https://scholar.google.dk/citations?user=qhVF9FMAAAAJ&hl=en

Philippe Lambert

  • Philippe Lambert is full professor of quantitative methods at ULiege and part-time professor of Bayesian biostatistics at UCLouvain (Belgium). He obtained a bachelor's degree in mathematics (1992) from the University of Liège, a master's degree (1994) and a PhD (1995) in biostatistics from the University of Hasselt. He publishes methodological work in various areas of statistical modelling, including Bayesian smoothing methods, survival analysis, interval censoring, Bayesian inference in dynamic models, copula dependence modelling and longitudinal data analysis, with applications in medicine, epidemiology, demography, sociology and actuarial science, resulting in more than 70 publications in international peer reviewed journals. He is Associate Editor of Biostatistics (Oxford U.P.) and Statistical Modelling (Sage).  
  • Google scholar: https://scholar.google.dk/citations?hl=en&user=K5SGdRUAAAAJ
  • ORCID: https://orcid.org/0000-0002-3670-3328
  • Personal website: http://www.statsoc.ulg.ac.be

Catherine Legrand

  • Catherine Legrand (Catherine.legrand@uclouvain.be) is Full Professor of statistics at the UCLouvain (Belgium). After having obtained a Master Degree in Mathematics from the Université Libre de Bruxelles (ULB), she worked for 7 years at the European Organization for Research and Treatment of Cancer (EORTC) and became the primary statistician of the EORTC Lung Cancer Group. She was also a member of the Treatment Outcome Research Group, the Elderly Task Force, and coordinator of the EORTC Independent Data Monitoring Committee. In parallel, she completed a PhD in 2005 at the Center for Statistics, Hasselt University, in the field of survival analysis (frailty models). Early 2006, she started working as biometrician at Merck Sharp & Dohme (MSD) where she was involved in the design and analysis of clinical trials in respiratory diseases. In 2007, she joined the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA-LIDAM) of the Université catholique de Louvain (UCLouvain). Her area of research includes survival data analysis and design of clinical trials. Along with these professional experiences, she co-authored more than 80 papers in peer-reviewed clinical and statistical journals and published in 2021 a book entitled “Advanced Survival Model” (Chapman and Hall/CRC). She is member of the Scientific Committee of the Belgian Fondation Contre le Cancer and Associate Editor for Biometrics.
  • Google Scholar: https://scholar.google.dk/citations?hl=en&user=xyQwjrUAAAAJ
  • Personal website: (under construction)

Eugen Pircalabelu

  • Eugen Pircalabelu (eugen.pircalabelu@uclouvain.be) is a Lecturer (Chargé de cours) at UCLouvain working at the Institute of Statistics, Biostatistics and Actuarial Sciences within LIDAM at the Faculty of Science since 2018. Prior to moving at UC Louvain, he held a Visiting professor position at Ghent University affiliated with the Department of Applied Mathematics, Computer Science and Statistics within the Faculty of Sciences and a Postdoctoral position at KU Leuven affiliated with the ORSTAT department within the Faculty of Economics and Business. His research focuses on: Models for high-dimensional data, Distributed estimation and inference, Probabilistic graphical models, Social network models, Copula and dependence modelling and Information criteria.
  • Google scholar: https://scholar.google.dk/citations?hl=en&user=6GUExvgAAAAJ
  • Personal website: https://perso.uclouvain.be/eugen.pircalabelu/

Germain Van Bever

  • Germain Van Bever (germain.vanbever@unamur.be) is Associate Professor of Statistics at the Université de Namur. He holds a master (2008) degree in Mathematics and a PhD (2012) in Statistics from the Université libre de Bruxelles. His research focuses on theoretical innovations in the context of nonparametric statistics (including depth-based methods), functional data analysis and high dimensional statistics. He is currently the author of 14 published papers (including contributions in the Annals of Statistics, Bernoulli and the Journal of the American Statistical Association), 5 peer-reviewed chapter of books and two submitted papers. He is currently an Associate Editor for Econometrics and Statistics (Part B: Statistics), the Bulletin of the Belgian Mathematical Society – Simon Stevin and Mathematical Reviews. He is also the President of the FNRS doctoral school in Statistics and Actuarial Sciences, as well as member of the board of the Royal Statistical Society of Belgium and the Belgium Mathematical Society. He obtained several awards, among which the annual prize from the royal Academy of Belgium. He is also an Elected member of the International Statistical Institute (2018).
  • Google scholar: https://scholar.google.dk/citations?hl=en&user=hnz7L-gAAAAJ
  • Personal website: https://sites.google.com/site/germainvanbever/home-1

Ingrid Van Keilgom

  • Ingrid Van Keilegom (Ingrid.vankeilegom@uclouvain.be) is Full Professor of Statistics at the UCLouvain and the KU Leuven.  She received a B.S. degree in mathematics (1993) from the Universiteit Antwerpen, and a master in biostatistics and a PhD in statistics (both in 1998) from the Universiteit Hasselt.   Her research focuses on the development of new methodology and theory in areas like survival analysis, causal inference, quantile regression, measurement errors, and non- and semiparametric regression, and this resulted in more than 160 publications in international peer-reviewed journals.  Ingrid Van Keilegom is currently holder of an ERC Advanced Grant (2016-2022), and has held in the past an ERC Starting Grant (2008-2014).  She has been joint editor of the Journal of the Royal Statistical Society–Series B (2012-2015).  Currently she is Associate Editor of Annals of Statistics (2018-), Biometrika (2017-), Annual Review of Statistics and Its Application (2016-), Electronic Journal of Statistics (2018-), and has been Associate Editor of several other journals in the past.  She is fellow of the American Statistical Association (2013) and of the Institute of Mathematical Statistics (2008).
  • Google Scholar: https://scholar.google.dk/citations?hl=en&user=6Sb63foAAAAJ
  • Personal website: (under construction)

Researchers involved in the project

  • Hortense Doms, PhD Student, 2020-present
  • Morine Delhelle, PhD Student, 2020-present
  • Benjamin Deketelaere, PhD student, 2020-present
  • Chikeola Ladepko, PhD student, 2020-2023
  • Jeon Jeong Min, Post-doc researcher, 50%, 2021-2022
  • Oskar Laverny, Post-doc researcher, 2022-present
  • Quentin Le Coënt, PhD student 2020-2023 and Post-doc researcher 2023-present

Research objectives and summary of progress

Still to be done

 

 

Research activities

Publications related to this ARC:

Books, as author, co-author or editor

  • Legrand, C. and Bertrand, A. Cure Models in Cancer Clinical trials. In: Halabi, S. and Michiels, S. (eds), Textbook of Clinical Trials in Oncology. A Statistical Perspective. First Edition. Chapman and Hall/CRC, Boca Raton. 2020.
  • Legrand, C. Advanced Survival Models. First Edition. Chapman and Hall/CRC, Boca Raton. 2021. Book chapters, as author or co-author
  • Bucher, A., El Ghouch, A. and Van Keilegom, I. Single-index quantile regression models for cen- sored data. In: Daouia, A. and Ruiz-Gazen, A. (eds.), Advances in Contemporary Statistics and Econometrics. Springer, New-York, 2021.
  • Delaigle, A. and Van Keilegom, I. Deconvolution with unknown error distri- bution. In: Yi, G., Delaigle, A. and Gustafson, P. (eds.), Handbook on Measurement Error Models, Chapman and Hall/CRC, 2021, Chapter 12, p. 245-270.
  • Conde-Amboage, M., Van Keilegom, I. and Gonzalez-Manteiga, W. Application of quantile regression models for biomedical data. In: Larriba, Y. (eds.), Statistical Methods at the Forefront of Biomedical Advances, Springer 2023 (to appear).

Articles published in peer-reviewed journals

  • De Backer, M., El Ghouch, A. and Van Keilegom, I. Linear censored quantile regression: a novel minimum-distance approach. Scand. J. Statist., 2020, 47, 1275-1306.
  • Barbieri A, Legrand C. Joint longitudinal and time-to-event cure models for the assessment of being cured. Stat Methods Med Res. 2020 Apr;29(4):1256-1270. doi: 10.1177/0962280219853599.
  • Chown, J., Heuchenne, C. and Van Keilegom, I. The nonparametric location- scale mixture cure model. TEST, 2020, 29, 1008-1028.
  • Florens, J.-P., Simar, L. and Van Keilegom, I. Estimation of the boundary of a variable observed with symmetric error. J. Amer. Statist. Assoc., 2020, 115, 425-441.
  • Gressani, O. and Lambert, P. Laplace approximation for fast Bayesian inference in generalized additive models based on P-splines. Computational Statistics and Data Analysis, 2021, 154, 107088.
  • Molenberghs, G., Buyse, M., Hens, N., Beutels, P., Faes, C., Verbeke, G., Van Damme, P., Goossens, H., Neyens, T., Abrams, S., Theeten, H., Pepermans, K., Alonso Abad, A., Van Keilegom, I., Speybroeck, N., Legrand, C., De Buyser, S. and Hulstaert, F. Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country. Controlled Clinical Trials, 2020, 99, Art. Nr. 106189.
  • Noh, H. and Van Keilegom, I. On relaxing the distributional assumption of stochastic frontier models. J. Korean Statist. Soc., 2020, 49, 1-14.
  • Chown, J., Heuchenne, C. and Van Keilegom, I. The nonparametric location- scale mixture cure model. TEST, 2020, 29, 1008-1028.
  • Deresa, N.W. and Van Keilegom, I. A multivariate normal regression model for survival data subject to different types of dependent censoring. Comp. Statist. Data Anal., 2020, 106879.
  • Racine, J. and Van Keilegom, I. A smooth nonparametric, multivariate, mixed- data location-scale test. J. Bus. Econ. Statist., 2020, 38, 784-795.
  • Geerdens, C., Janssen, P. and Van Keilegom, I. Goodness-of-fit test for a parametric survival function with cure fraction. TEST, 2020, 29, 768-792.
  • Zhao, Y., Gijbels, I. and Van Keilegom, I. Inference for semiparametric Gaussian copula model adjusted for linear regression using residual ranks. Bernoulli, 2020, 26, 2815- 2846.
  • Patilea, V. and Van Keilegom, I. A general approach for cure models in survival analysis. Ann. Statist., 2020, 48, 2323-2346.
  • Lopez-Cheda, A., Jacome-Pumar, A., Van Keilegom, I. and Cao, R. Nonparametric covariate hypothesis tests for the cure rate in mixture cure models. Statist. Med., 2020, 39, 2291-2307.
  • Florens, J.-P., Simar, L. and Van Keilegom, I. Estimation of the boundary of a variable observed with symmetric error. J. Amer. Statist. Assoc., 2020, 115, 425-441.
  • Deresa, N.W. and Van Keilegom, I. Flexible parametric model for survival data subject to dependent censoring. Biometr. J., 2020, 62, 136-156.
  • Delsol, L. and Van Keilegom, I. Semiparametric M-estimation with non-smooth criterion functions. Ann. Inst. Statist. Math., 2020, 72, 577-605.
  • Bravo, F., Escanciano, J.C. and Van Keilegom, I. Two-step semiparametric empirical likelihood inference. Ann. Statist., 2020, 48, 1-26.
  • Colling, B. and Van Keilegom, I. Estimation of a semiparametric transformation model : a novel approach based on least squares minimization. Electr. J. Statist., 2020, 14, 769-800.
  • Cantagallo, E., De Backer, M., Kicinski, M., Ozenne, B., Collette, L., Legrand, C., Buyse, M., Péron ,J. A new measure of treatment effect in clinical trials involving competing risks based on generalized pairwise comparisons. Biometrical Journal, 2021 , 63, 272-288.
  • Helander, S., Laketa, P., Ilmonen, P., Nagy, S., Van Bever, G., and Viitasaari, L. Integrated shape- sensitive functional metrics. Journal of Multivariate Analysis, 2021, 189, 104880.
  • Lambert, P. Fast Bayesian inference using Laplace approximations in nonparametric double additive location-scale models with right- and interval-censored data. Computational Statistics and Data Analysis, 2021, 161, 107250.
  • Le Coënt, Q., Legrand, C., Rondeau, V. Time-to-event surrogate endpoint validation using mediation analysis and meta-analytic data. Biostatistics, 2022; kxac044, https://doi.org/10.1093/biostatistics/kxac044
  • Nagy, S., Helander, S., Viitasaari, L., Van Bever, G. and Ilmonen, P. Flexible integrated functional depths. Bernoulli, 2021, 27, 1.
  • Pircalabelu, E. and Artemiou, A (2021). Graph informed sufficient dimension reduction. Computational Statistics and Data Analysis, 2021, 164, 107302.
  • Jacquemain, A., Heuchenne, C., & Pircalabelu, E. A lasso-type estimation for the Lorenz regression. Proceedings of the 22nd European Young Statistician Meeting, September 6-10th 2021, Athens. Panteion University. Pages 41-45.
  • Beyhum, J., Florens, J.-P. and Van Keilegom, I. Nonparametric instrumental regression with right censored duration outcomes. J. Bus. Econ. Statist., 2021, 40, 1034- 1045.
  • Florez, A.J., Van Keilegom, I., Molenberghs, G. and Verhasselt, A. Quantile regression for longitudinal data via the multivariate generalized hyperbolic distribution. Statist. Modelling, 2021, 22, 566-584.
  • Jeon, J.M., Park, B.U. and Van Keilegom, I. Additive regression for non- Euclidean responses and predictors. Ann. Statist., 2021, 49, 2611-2641.
  • Escobar-Bach, M., Maller, R., Van Keilegom, I. and Zhao, M. Estimation of the cure rate for distributions in the Gumbel maximum domain of attraction under insufficient follow-up. Biometrika, 2021, 109, 243-256.
  • Verhasselt, A., Flo ́rez, A.J., Van Keilegom, I. and Molenberghs, G. (2021). The impact of incomplete data on quantile regression for longitudinal data. J. Statist. Research, 2021, 55, 43-58.
  • Kloodt, N., Neumeyer, N. and Van Keilegom, I. Specification testing in semi- parametric transformation models. TEST, 2021, 30, 980-1003.
  • Conde-Amboage, M., Van Keilegom, I. and Gonzalez-Manteiga, W. A new lack- of-fit test for quantile regression with censored data. Scand. J. Statist., 2021, 48, 655-688.
  • Deresa, N. and Van Keilegom, I. On semiparametric modelling, estimation and inference for survival data subject to dependent censoring. Biometrika, 2021, 108, 965-979.
  • Amico, M., Van Keilegom, I. and Han, B. Assessing cure status prediction from survival data using receiver operating characteristic curves. Biometrika, 2021, 108, 727-740.
  • Musta, E. and Van Keilegom, I. A simulation-extrapolation approach for the mixture cure model with mismeasured covariates. Electr. J. Statist., 2021, 15, 3708-3742.
  • Jeon, J.M., Park, B. and Van Keilegom, I. Additive regression for predictors of various natures and possibly incomplete Hilbertian responses. Electr. J. Statist, 2021, 15, 1473-1548.
  • de Una Alvarez, J. and Van Keilegom, I. Efron-Petrosian integrals for doubly truncated data with covariates: an asymptotic analysis. Bernoulli, 2021, 27, 249-273.
  • Garcia-Barrado, L., Burzykowski, T., Legrand, C., and Buyse, M. Using an interim analysis based exclusively on an early outcome in a randomized clinical trial with a long-term clinical endpoint. Pharmaceutical Statistics, 2022, 21(1): 209-2019.
  • Soetewey, A., Legrand, C, Denuit, M., & Silversmit, G. Semi-Markov modeling for cancer insurance. European Actuarial Journal 2022, 12, 813-837.
  • De Backer, M., Legrand, C., Péron, J., Lambert, A., Buyse M. On the use of Extreme Value Tail Modeling for Generalized Pairwise Comparisons with Censored Outcomes. Pharmaceutical Statistics, 2022. https://doi.org/10.1002/pst.2271
  • Pircalabelu, E. & Artemiou, A. "High-dimensional Sufficient Dimension Reduction through principal projections", Electronic Journal of Statistics, 2022, 16(1), 1804-1830.
  • Zhao, Y., Van Keilegom, I. and Ding, S. Envelopes for censored quantile re- gression. Scand. J. Statist., 2022, 49, 1562-1585.
  • Jeon, J.M., Park, B. and Van Keilegom, I. Nonparametric regression on Lie groups with measurement errors. Ann. Statist., 2022, 50, 2973-3008.
  • Deresa, N.W., Van Keilegom, I. and Antonio, K. Copula-based inference for bivariate survival data with left truncation and dependent censoring. Insur.: Math. Econ., 2022, 107, 1-21.
  • Kreiss, A. and Van Keilegom, I. Semi-parametric estimation of incubation and generation times by means of Laguerre polynomials. J. Nonpar. Statist., 2022, 34, 570-606.
  • Musta, E., Patilea, V. and Van Keilegom, I. A presmoothing approach for estimation in the semiparametric Cox mixture cure model. Bernoulli, 2022, 28, 2689-2715.
  • Venturini, M., Van Keilegom, I., De Corte, W. and Vens, C. A novel survival analysis approach to predict the need for intubation in intensive care units. Artif. Intell. Medic., 2022, 13263.
  • Han, B., Van Keilegom, I. and Wang, X. Semiparametric estimation of the non-mixture cure model with auxiliary survival information. Biometrics, 2022, 78, 448-459.
  • Zhao, Y., Gijbels, I. and Van Keilegom, I. Parametric copula adjusted for non- and semi-parametric regression. Ann. Statist., 2022, 50, 754-780.
  • Kekec, E. and Van Keilegom, I. (2022). Estimation of the variance matrix in bivariate classical measurement error models. Electr. J. Statist, 16, 1831-1854.
  • Beyhum, J., El Ghouch, A., Portier, F. and Van Keilegom, I. On an extension of the promotion time cure model. Ann. Statist., 2022, 50, 537-559.
  • Kreyenfeld, M., Konietzka, D., Lambert, P. and Ramos, .V. Second birth fertility in Germany: social class, gender, and the role of economic uncertainty. European Journal of Population, 2023, 39:5.
  • Lambert, P. Nonparametric density estimation and risk quantification from tabulated sample moments. Insurance: Mathematics and Economics, 2023, 108: 177-189.
  • Pircalabelu, E. & Claeskens, G. Linear manifold modeling and graph estimation based on multivariate functional data. Journal of Computational and Graphical Statistics. 2023, 32(2), 378-387.
  • Nezakati, E. & Pircalabelu, E. Unbalanced distributed estimation and inference for precision matrices. Statistics and Computing, 2023, 33, 47.
  • Pircalabelu, E. A spline-based time-varying reproduction number for modelling epidemiological outbreaks. Journal of the Royal Statistical Society (C), 2023, 72(3), 688–702.
  • Beyhum, J., Florens, J.-P. and Van Keilegom, I. A nonparametric instrumental approach to endogeneity in competing risks models. Lifetime Data Anal. 2023. (to appear).
  • Kekec, E. and Van Keilegom, I. Variance matrix estimation in multivariate classical measurement error models. Statist. Papers, 2023 (to appear).
  • Tedesco, L. and Van Keilegom, I. Comparison of quantile regression curves with censored data. TEST, 2023 (to appear).
  • Escobar-Bach, M. and Van Keilegom, I. Nonparametric estimation of conditional cure models for heavy-tailed distributions and under insufficient follow-up. Comput. Statist. Data Anal. 2023. (to appear).
  • Beyhum, J., Tedesco, L. and Van Keilegom, I. Instrumental variable quantile regression under random right censoring. Econometrics Journal 2023 (to appear).
  • Czado, C. and Van Keilegom, I. Dependent censoring based on parametric copulas. Biometrika 2023 (to appear).
  • Fanjul-Hevia, A., Pardo-Fern ́andez, J.C., Van Keilegom, I. and Gonzalez-Manteiga, W. (2023). A test for comparing conditional ROC curves with multidimensional covariates. J. Appl. Statist. 2023 (to appear).
  • Parsa, M. and Van Keilegom, I. Accelerated failure time vs Cox proportional hazards mixture cure models: David vs Goliath? Statist. Papers 2023 (to appear).
  • Beyhum, J., Florens, J.-P. and Van Keilegom, I. Discussion on “Instrumented difference-in-differences” by T. Ye, A. Ertefaie, J. Flory, S. Hennessy, and D.S. Small. Biometrics 2023 (to appear).
  • Gonzalez Manteiga, W., Martınez Miranda, M.D. and Van Keilegom, I. Goodness- of-fit tests in proportional hazards models with random effects. Biometr. J. 2023 (to appear).
  • Beyhum, J. and Van Keilegom, I. Robust censored regression with l1-norm regularization. TEST, 2023, 32, 146-162.
  • Jeon, J.M. and Van Keilegom, I. Density estimation for mixed Euclidean and non-Euclidean data in the presence of measurement error. J. Multiv. Anal., 2023, 193, 105125.

Seminars/oral presentations by members of the ARC project:

  • C. Legrand. On the use of cure models in cancer clinical trials. Webinaire Unité Mixte de Recherche SESSTIM Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (Aix Marseille Université), March 20, 2020.
  • C. Legrand. On the use of cure models in cancer clinical trials. Invited Session Speaker. 30th International Biometric Conference – Seoul, South Korea July 2020 (organized online due to covid-19 pandemic)
  • Pircalabelu, E. Graph informed sufficient dimension reduction, CMStatistics Conference, 2020 (virtual).
  • Van Bever, G. Adaptive integrated functional depth, CMStatistics Conference, 2020 (virtual).
  • C. Legrand. The Single-Index/Cox Mixture Cure Model. Invited Session Speaker. JDS 2021: 52èmes Journées de Statistique de la Société Française de Statistique (SDdS)- Nice, June 7-11 , 2021 (organized online due to covid-19 pandemic)
  • Le Coënt Q., Legrand C., Rondeau V. Causal assessment of surrogacy for time-to-event endpoints using meta-analytic data. 8th Channel Network Conference, Paris, France, 7-9 April 2021.
  • Lambert, P. Nonparametric location-scale models for right- and interval-censored data with inference based on Laplace approximations. Invited research seminar at the University of Sherbrooke, Québec, Canada, 30 November 2021. (Online seminar)
  • Legrand, C. The Single-Index/Cox Mixture Cure Model, JDS 2021: 52èmes Journées de Statistique de la Société Francaise de Statistique (SDdS), 2021, Nice, France (virtual) - Invited speaker.
  • Pircalabelu, E. Unbalanced distributed estimation and inference for Gaussian graphical models, October Math Symposium at UNC Charlotte, 2021, Charlotte, USA (virtual).
  • Pircalabelu, E. Unbalanced distributed estimation and inference for covariate-adjusted Gaussian graphical models, CMStatistics Conference, 2021, London, UK (hybrid).
  • Van Bever, G. Flexible integrated functional depth, CMStatistics Conference, 2021, London, UK (hybrid).
  • Van Bever, G. On optimal prediction of missing functional data with memory. 4th International Conference on Econometrics and Statistics ECOSTA, 2021, Hong-Kong, China (virutal).
  • Van Keilegom, I. Dependent censoring based on copulas, CMStatistics Conference, 2021, London, UK (hybrid) - Invited speaker.
  • Van Keilegom, I. Nonparametric instrumental regression with right censored duration outcomes, Hong Kong Baptist University (HKBU) Mathematics conference for Faculty of Science 60th Anniver- sary, Hong Kong, China 2021 (virtual) - Invited speaker.
  • Van Keilegom, I. On a semiparametric estimation method for AFT mixture cure models, 22nd Euro- pean Young Statisticians Meeting, 2021, Athens, Greece (virtual) - Keynote speaker.
  • Van Keilegom, I. On a semiparametric estimation method for AFT mixture cure models, Bernoulli- IMS 10th World Congress in Probability and Statistics, 2021, Seoul, South Korea (virtual) - Invited speaker.
  • Van Keilegom, I. Dependent censoring based on copulas, 63rd ISI World Statistics Congress, 2021 (virtual) - Invited speaker.
  • Van Keilegom, I. On a semiparametric estimation method for AFT mixture cure models, NORD- STAT 2021, The 28th Nordic Conference in Mathematical Statistics, 2021, Tromso, Norway (hybrid) - Keynote speaker.
  • Van Keilegom, I. Instrumental variables in duration models, 8th Days of Econometrics for Finance (JEF2021), 2021 (virtual) - Keynote speaker.
  • Van Keilegom, I. Cure models in survival analysis. Invited research seminar at Faculty of Medicine at KULAK, KU Leuven, Belgium, 21 December 2021. (Hybrid seminar)
  • Van Keilegom, I. Nonparametric instrumental regression with right censored duration outcomes. Invited research seminar at Department of economics, Brown University, USA, 7 December 2021. (Online seminar)
  • Van Keilegom, I. Dependent censoring based on copulas. Invited seminar at Department of mathematics, Indiana University-Purdue University Indianapolis, USA, 24 August 2021. (Online seminar)
  • Van Keilegom, I. Instrumental variables in duration models. Invited seminar at Department of eco- nomics, University of Wisconsin, Madison, USA, 19 March 2021. (Online seminar)
  • Van Keilegom, I. On a semiparametric estimation method for AFT mixture cure models. Invited seminar at Department of statistics, Instituto Technologico Autonomo de Mexico, Mexico, 12 February 2021. (Online seminar)
  • Doms, H., Lambert, P. and Legrand, C. Flexible joint model for time-to-event and non-Gaussian longitudinal outcomes. 36th International Workshop on Statistical Modelling, Trieste, Italy, 18-22 July, 2022.
  • Lambert, P. and Kreyenfeld, M. Laplace approximation for penalty selection in double additive cure survival model with exogenous time-varying covariates. 36th International Workshop on Statistical Modelling, Trieste, Italy, 18-22 July, 2022.
  • Lambert, P. Laplace approximations and Bayesian P-splines in nonparametric location-scale models for interval-censored data. Invited seminar at the University of Aix-Marseille, France, 16 May 2022 (virtual).
  • Pircalabelu, E. High-dimensional sufficient dimension reduction through principal projections. CMStatistics, December 17-19, 2022. London, UK.
  • Pircalabelu, E. Community detection on probabilistic graphical models with group-based penalties. ISNPS, 2022, June 22-24. Paphos, Cyprus.
  • Pircalabelu, E. Unbalanced distributed estimation and inference for (covariate-adjusted) Gaussian graphical models. Statistics and Econometrics Seminars, February 17th, 2022. KU Leuven
  • Pircalabelu, E. Unbalanced distributed estimation and inference for (covariate-adjusted) Gaussian graphical models. Departamento de Estatística, Análisis Matemática y Optimización, February 23rd, 2022. Universidade de Santiago de Compostela, Spain.
  • Pircalabelu, E. Linear manifold modelling and graph estimation based on multivariate functional data with different coarseness scales. Statistical Modelling with Applications, October 14-15, 2022. Bucharest, Romania
  • Van Keilegom, I. Nonparametric instrumental regression with right censored duration outcomes. Winter Conference of the Korean Statistical Society, Jeju, South Korea, 1-3 December 2022 (keynote).
  • Van Keilegom, I. Instrumental variable quantile regression under random right censoring. 5th Workshop on Goodness-of-fit and change-point problems, ENSAI, Rennes, France, 2-4 September 2022.
  • Van Keilegom, I. Dependent censoring based on copulas. Joint Statistical Meetings, Washington, US, 7-11 August 2022.
  • Van Keilegom, I. Discussant in section on ‘Flexible extensions of the accelerated failure time model’. 2022 International Biometric Conference (IBC), Riga, Latvia, 11-15 July 2022:.
  • Van Keilegom, I. Nonparametric instrumental regression with right censored duration outcomes. International Symposium on Nonparametric Statistics (ISNPS), Paphos, Cyprus, 20- 24 June 2022.
  • Van Keilegom, I. Dependent censoring based on copulas. MDSA2022 (Missing Data and Survival Analysis), Angers (hybrid), 30 May-1 June 2022.
  • Pircalabelu, E. Graph estimation based on multivariate functional data with different coarseness scales. Romanian Society of Probability and Statistics, April 21-22, 2023. Bucharest, Romania
  • Pircalabelu, E. Graph estimation based on multivariate functional data with different coarseness scales. Asymptotic Theory for Multidimensional Statistics Workshop, May 3-5, 2023. Leuven, Belgium
  • Pircalabelu, E. Distributed estimation and inference for Gaussian graphical models under an unbalanced distributed setting. Smart Diaspora, April 10-13, 2023. Timisoara, Romania
  • Pircalabelu, E. Aggregating estimators from distributed sources. A journey from estimation to model selection. EDT, UNamur, May 30, 2023. Namur, Belgium
  • Van Keilegom, I. Instrumental variable estimation of dynamic treatment effects on a survival outcome. 2023 Lifetime Data Science Conference, Raleigh, USA, 31 May-2 June 2023.
  • Van Keilegom, I. An introduction to dependent censoring. ATMS (Asymptotic Theory for Multidimensional Statistics) workshop, KU Leuven, 3-5 May 2023.
  • Van Keilegom, I. Instrumental variable estimation of dynamic treatment effects on a survival outcome. 2nd Workshop on High-Dimensional Data Analysis, Carlos III University, Madrid, 2- 3 March 2023 (keynote).

Seminars/Short courses organized in the context this ARC:

  • Statistics seminars - Alexander Kreiss, KU Leuven ”Correlation bounds, mixing and m- dependence under random time-varying network distances with an application to Cox-Processes”. 09-10-2020
  • Statistics seminars - Andreas Artemiou, Cardiff University ”SVM-based real time sufficient dimension reduction”. 24-09-2021
  • Statistics seminars - Michael Lalancette, University of Toronto : ”The extremal graphical lasso”
  • Statistics seminars - Jad Beyhum, KU Leuven : ”Nonparametric Instrumental Regression With Right Censored Duration Outcomes”. 15-10-2021
  • Short course - Modeling Survival Outcomes with High Dimensional Predictors: Methods and Applications. Yi Li, Professor of Biostatistics, University of Michigan. (05-07-2021, 2pm-6pm - online)
  • Statistics seminars – Roch Giorgi, Aix-Marseille University : ” Extending excess hazard regression model in the absence of appropriate life tables”