See also my profile on Google Scholar.


ANOVA and Mixed Models: A Short Introduction Using R (to appear)
Please visit the book's website for more information.

Logistische Regression: Eine anwendungsorientierte Einführung mit R
(with M. Kalisch), Springer Open Access, 2021.
Please visit the book's website for more information.

Wahrscheinlichkeitsrechnung und Statistik: Eine Einführung für Verständnis, Intuition und Überblick
Springer, 2020.
Please visit the book's website for more information.

Research Papers

  1. Anderegg, N., Hector, J. , Jefferys, L.F., Burgos-Soto, J., Hobbins, M.A., Ehmer, J., Meier, L., Maathuis, M.H. and Egger, M. (2020). Loss to follow-up correction increased mortality estimates in HIV-positive people on antiretroviral therapy in Mozambique. Journal of Clinical Epidemiology, 128, 83-92. Online Access.
  2. Klasen, J., Barbez, E., Meier, L., Meinshausen, N., Bühlmann, P., Koornneef, M., Busch, W. and Schneeberger, K. (2016) . A multi-marker association method for genome-wide association studies without the need for population structure correction. Nat Commun 7, 13299. Online Access.
  3. de Matos, N. M. P., Meier, L., Wyss, M., Meier, D., Gutzeit, A., Ettlin, D. A. and Brügger, M. (2016). Reproducibility of Neurochemical Profile Quantification in Pregenual Cingulate, Anterior Midcingulate, and Bilateral Posterior Insular Subdivisions Measured at 3 Tesla, Frontiers in Human Neuroscience, 10, Online Access.
  4. Meier, L. (2016). High-Dimensional Regression and Inference. In P. Bühlmann, P. Drineas, M. Kane and M.J. van der Laan (Eds.), Handbook of Big Data, 305-319. Chapman and Hall/CRC, Boca Raton, FL.
  5. Dezeure, R., Bühlmann, P., Meier, L. and Meinshausen, N. (2015). High-dimensional inference: confidence intervals, p-values and R-software hdi. Statistical Science, 30, 533-558. Online Access.
  6. Bühlmann, P., Meier, L. and van de Geer, S. (2014). Discussion on "A significance test for the Lasso (R. Lockhart, J. Taylor, R. Tibshirani and R. Tibshirani)". Annals of Statistics 42, 469-477. Online Access.
  7. Bühlmann, P., Kalisch, M. and Meier, L. (2014). High-Dimensional Statistics with a View Toward Applications in Biology. Annual Review of Statistics and its Applications 1, 255-278. Online Access.
  8. Schelldorfer, J., Meier, L. and Bühlmann, P. (2013). GLMMLasso: An algorithm for high-dimensional generalized linear mixed models using L1-penalization. Journal of Computational and Graphical Statistics. Online Access.
  9. Nicolai Meinshausen, Lukas Meier and Peter Bühlmann (2008). P-values for High-Dimensional Regression. Journal of the American Statistical Association 104, 1671-1681. Online Access.
  10. Meier, L., van de Geer, S. and Bühlmann, P. (2009). High-Dimensional Additive Modeling. Annals of Statistics 37, 3779-3821. Online Access.
  11. Hesterberg, T., Choi, N.H., Meier, L. and Fraley C. (2008) Least Angle and $\ell_1$ Penalized Regression: A Review. Statistic Surveys 2, 61-93 (electronic). Online Access.
  12. Schöner, D., Kalisch, M., Leisner, C., Meier, L., Sohrmann, M., Faty, M., Barral, Y., Peter, M., Gruissem, W. and Bühlmann, P. 2008. Annotating novel genes by integrating synthetic lethals and genomic information, BMC Systems Biology, 2:3. Online Access.
  13. Bühlmann, P. and Meier, L. (2008). Discussion on "One-step sparse estimates in nonconcave penalized likelihood models (H. Zou and R. Li)". Annals of Statistics, Volume 36, Number 4, 1534-1541. Online Access.
  14. Meier, L. and Bühlmann, P. (2007). Smoothing $\ell_1$-penalized estimators for high-dimensional time-course data, Electronic Journal of Statistics 1, 597-615 (electronic). Online Access.
  15. Meier, L., van de Geer, S. and Bühlmann P. 2008. The group lasso for logistic regression, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70 (1), 53-71. Online Access.