Institutes

Members of the Research Center for Statistics publish scientific work that is primarily in the area of fundamental statistics (mathematical statistics) and focuses on applied research areas such as financial econometrics, economics, health sciences, engineering, environmental sciences, psychology, and social sciences. In particular, the Center's researchers provide expertise in robust inference, small sample inference, indirect inference, semi-parametric and non-parametric statistics, model selection for high-dimensional data, time series analysis, linear latent variable and mixture models, and longitudinal data analysis.

The Center's membership includes individuals who are (or have been) members of the editorial boards of statistical journals such as the Journal of the American Statistical Association, TEST, Sankhya B, Computational Statistics & Data Analysis, and other disciplinary journals such as The Journal of Income Inequality.

The Center also organizes or participates in the organization of international scientific conferences. These include the 21st International Conference on Computational Statistics (COMPSTAT 2014), held in Geneva in August 2014, and the International Conference on Robust Statistics (ICORS 2016), held in Geneva in June 2016.

The members of the Research Center for Statistics are involved in seeking external funding for research projects. Many of the Center's Ph.D. students are funded through these projects. Financial support comes mainly from the Swiss National Science Foundation, although some funding comes from other funds.

 

Selected Publications

Kramlinger, P., Krivobokova, T., & Sperlich, S. (2022). Marginal and Conditional Multiple Inference for Linear Mixed Model Predictors. Journal of the American Statistical Association.
doi.org/10.1080/01621459.2022.2044826

La Vecchia, D., Moor, A., & Scaillet, O. (2022). A higher-order correct fast moving-average bootstrap for dependent data. Journal of Econometrics.
https://doi.org/10.1016/j.jeconom.2022.01.008

Lideikyte-Huber, G., & Pittavino, M. (2022). Who donates and how? New evidence on the tax incentives in the canton of Geneva, Switzerland. Journal of Empirical Legal Studies.
https://doi.org/10.1111/jels.12322

Mammen, E., & Sperlich, S. (2022). Backfitting tests in generalized structured model. Biometrika, 109(1), 137–152.
https://doi.org/10.1093/biomet/asaa108

Gnecco N., Meinshausen, N., Peters, J., & Engelke, S. (2021). Causal discovery in heavy-tailed models. The Annals of Statistics, 49(3), 1755–1778.
https://doi.org/10.1214/20-AOS2021

Guerrier, S., Molinari, R., Victoria-Feser, M.-P., & Xu, H. (2021). Robust two-step wavelet-based inference for time series models. Journal of the American Statistical Association.
https://doi.org/10.1080/01621459.2021.1895176

Jiang, C., La Vecchia, D., Ronchetti, E., & Scaillet, O. (2021). Saddlepoint approximations for spatial panel data models. Journal of the American Statistical Association.
https://doi.org/10.1080/01621459.2021.1981913

Lalancette, M., Engelke, S., & Volgushev, S. (2021). Rank-based Estimation under Asymptotic Dependence and Independence, with Applications to Spatial Extremes. The Annals of Statistics, 49(5), 2552–2576.
https://doi.org/10.1214/20-AOS2046

Peñaranda, F., Rodríguez-Poo, J. M., & Sperlich, S. (2021). Nonparametric specification testing of conditional asset pricing models. Journal of Business & Economic Statistics.
https://doi.org/10.1080/07350015.2021.1933500

Reluga, K., Lombardía, M.-J., & Sperlich, S. (2021). Simultaneous inference for empirical best predictors with a poverty study in small areas. Journal of the American Statistical Association.
https://doi.org/10.1080/01621459.2021.1942014

Engelke, S., & Hirtz A. S. (2020). Graphical models for extremes. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(4).
https://doi.org/10.1111/rssb.12355

Hallin, M., & La Vecchia, D. (2020). A simple R-estimation method for semiparametric duration models. Journal of Econometrics, 218(2), 736-749.
https://doi.org/10.1016/j.jeconom.2020.04.036

Hallin, M., La Vecchia, D., & Liu, H. (2020). Center-outward R-estimation for semiparametric VARMA models. Journal of the American Statistical Association.
https://doi.org/10.1080/01621459.2020.1832501

Guerrier, S., Dupuis-Lozeron, E., Ma, Y., & Victoria-Feser, M.-P. (2019). Simulation based bias correction methods for complex models. Journal of the American Statistical Association Theory and Methods Section, 114(525), 146–157.
https://doi.org/10.1080/01621459.2017.1380031

Avella-Medina, M., & Ronchetti, E. (2018). Robust and consistent variable selection in high-dimensional generalized linear models. Biometrika, 105(1), 31–44.
https://doi.org/10.1093/biomet/asx070

Cantoni, E., Mills Flemming, J., & Welsh, A. H. (2017). A random-effects hurdle model for predicting bycatch of endangered marine species. Annals of Applied Statistics, 11(4), 2178–2199.
https://doi.org/10.1214/17-AOAS1074

Hallin, M., & La Vecchia, D. (2017). R-estimation in semiparametric dynamic location-scale models. Journal of Econometrics, 196(2), 233–247.
https://doi.org/10.1016/j.jeconom.2016.08.002

 

> For a complete list, please visit our Knowledge & Publications page.

 

Recent Ph.D. Theses

 

Causal Inference for Extremes (Gnecco, N. 2022)

Contributions to higher-order correct and robust inference for dependent data (Moor, A. 2022)

Domain-Tailored Approaches to Statistical Learning (Bakalli, G. 2021)

Contributions to high-dimensional and semiparametric statistics for dependent data (Bodelet, J. 2021)

Statistical Inference on Network Data: Spatial Panel and Latent Variables (Jiang, C. 2021)

Topics in Statistics and Financial Econometrics: Penalized Estimators and Stochastic Discount Factors (Quaini, A. 2021)

Rare Events, Data Science and Climate Modeling (Vignotto, E. 2021)

Contributions to time series analysis (Xu, H. 2021)

Simultaneous and post-selection inference for mixed parameters (Reluga, K. 2020)

Contributions to simulation-based estimation models (Orso, S. 2019)

Permutation tests and multiple comparisons in the linear models and mixed linear models, with extension to experiments using electroencephalography (Frossard, J. 2019)


> Click here for more information on the Ph.D. in Statistics program.

   AACSB-logo-member-color-RGB.png        AMBA-logo-Acc-Colour.gif          EFMD-NewLogo2013-HR_colours.png        

prme-stacked-solid-rgb.png 
  GBSNLogo.png