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Working Group Biostatistics

Our group was funded from 2010-2016 through the Emmy Noether Programme of the German Research Foundation (DFG) as an Emmy Noether Junior Research Group "Statistical Methods for Longitudinal Functional Data". Publications from this project can be found here.

Publications since 2010

  • Happ C, Greven S (2016+): Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains. Journal of the American Statistical Association, to appear. Preprint.
  • Greven S, Scheipl F (2016+): Comment on Smoothing parameter and model selection for general smooth models by Simon Wood, Natalya Pya and Benjamin Säfken. Journal of the American Statistical Association, to appear.
  • Greven S, Scheipl F (2016+): A General Framework for Functional Regression Modelling. Invited discussion paper, to appear in Statistical Modelling.
  • Scheipl F, Gertheiss J, Greven S (2016): Generalized Functional Additive Mixed Models. Electronic Journal of Statistics, 10(1): 1455-1492.
  • Brockhaus S, Melcher M, Leisch F and Greven S (2016+): Boosting flexible functional regression models with a high number of functional historical effects. Statistics and Computing, accepted.
  • Scheipl F, Greven S (2016): Identifiability in penalized function-on-function regression models. Electronic Journal of Statistics, 10(1): 495-526.
  • Cederbaum J, Pouplier M, Hoole P, Greven S (2016): Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data. Statistical Modelling, 16(1): 67-88.
  • Augustin NH, Mattocks C, Faraway JJ, Greven S and Ness AR (2016+): Modelling a response as a function of high frequency count data: the association between physical activity and fat mass. Statistical Methods in Medical Research, accepted. arXiv.
  • Shou H, Zipunnikov V, Crainiceanu C, Greven S (2015): Structured Functional Principal Component Analysis. Biometrics, 71: 247-257. arXiv
  • Scheipl F, Staicu A-M, Greven S (2015): Functional Additive Mixed Models. Journal of Computational and Graphical Statistics, 24(2), 477-501. arXiv
  • Brockhaus S, Scheipl F, Hothorn T und Greven S (2015): The Functional Linear Array Model. Statistical Modelling, 15(3): 279-300.
  • Ivanescu AE, Staicu A-M, Scheipl F and Greven S (2015): Penalized function-on-function regression. Computational Statistics, 30(2): 539-568.
  • Fuchs K, Scheipl F and Greven S (2015): Penalized scalar-on-functions regression with Interaction Term. Computational Statistics & Data Analysis, 81: 38-51.
  • Obermaier V, Scheipl F, Heumann C, Wassermann J, Küchenhoff H (2015): Flexible Distributed Lags for Modeling Earthquake Data. Journal of the Royal Statistical Society, Series C, 64(2): 395-412.
  • Zipunnikov V, Greven S, Shou H, Caffo B, Reich D. and Crainiceanu C (2014): Longitudinal High-Dimensional Principal Components Analysis with Application to Diffusion Tensor Imaging of Multiple Sclerosis. Annals of Applied Statistics, 8(4): 2175-2202. arXiv
  • McLean M, Hooker G, Staicu A-M, Scheipl F and Ruppert D (2014): Functional Generalized Additive Models. Journal of Computational and Graphical Statistics, 23(1): 249-269.
  • Saefken, B, Kneib, T, van Waveren, C-S and Greven S (2014): A Unifying Approach to the Estimation of the Conditional Akaike Information in Generalized Linear Mixed Models. Electronic Journal of Statistics, 8: 201-225.
  • Goldsmith J and Scheipl F (2013): Estimator Selection and Combination in Scalar-on-Function Regression. Computational Statistics and Data Analysis, 70: 362-372.
  • Greven S and Crainiceanu C (2013): On Likelihood Ratio Testing for Penalized Splines. AStA Advances in Statistical Analysis, 97(4): 387-402.
  • Scheipl F, Kneib T, Fahrmeir L (2013): Penalized Likelihood and Bayesian Function Selection in Regression Models. AStA Advances in Statistical Analysis, 97(4): 349-385. arXiv
  • Wood SN, Scheipl F, Faraway JJ (2013): Straightforward intermediate rank tensor product smoothing in mixed models. Statistics and Computing, 23(3):341-360.
  • Gertheiss J, Goldsmith J, Crainiceanu C and Greven S (2013): Longitudinal Scalar-on-Functions Regression with Application to Tractography Data. Biostatistics, 14(3): 447-461. Preprint.
  • Goldsmith J, Greven S and Crainiceanu C (2013): Corrected Confidence Bands for Functional Data Using Principal Components. Biometrics, 69(1): 41-51. Preprint.
  • Baumert J, Karakas M, Greven S, Rückerl R, Peters A, Koenig W (2012): Variability of fibrinogen measurements in post-myocardial infarction patients: Results from the AIRGENE study center Augsburg. Thrombosis and Haemostasis, 107(5): 895-902.
  • Wiencierz A, Greven S and Küchenhoff H (2011): Restricted Likelihood Ratio Testing in Linear Mixed Models with General Error Covariance Structure. Electronic Journal of Statistics, 5: 1718-1734.
  • Greven S, Dominici F and Zeger S (2011): An Approach to the Estimation of Chronic Air Pollution Effects Using Spatio-Temporal Information. Journal of the American Statistical Association, 106(494): 396–406.
  • Greven S, Crainiceanu C, Caffo B and Reich D (2010): Longitudinal Functional Principal Component Analysis. Electronic Journal of Statistics, 4: 1022-1054. R function LFPCA
  • Greven S and Kneib T (2010): On the Behaviour of Marginal and Conditional AIC in Linear Mixed Models. Biometrika, 97(4): 773-789. R package cAIC
  • Tutz G and Gertheiss J (2010): Feature Extraction in Signal Regression: A Boosting Technique for Functional Data Regression. Journal of Computational and Graphical Statistics, 19: 154-174.

    • Papers in Proceedings

  • Greven S (2015): A General Framework for Functional Regression. Proceedings of the 30th International Workshop on Statistical Modelling, 51-66. Eds.: Friedl H and Wagner H.
  • Brockhaus S, Fuest A, Mayr A and Greven S (2015): Functional Regression Models for Location, Scale and Shape Applied to Stock Returns. Proceedings of the 30th International Workshop on Statistical Modelling, 117–122. Eds.: Friedl H and Wagner H.
  • Cederbaum J, Greven S, Pouplier M and Hoole P (2014): Functional Linear Mixed Model for Irregularly Spaced Phonetics Data. Proceedings of the 29th International Workshop on Statistical Modelling, 75–80. Eds.: Kneib T, Sobotka F, Fahrenholz J and Irmer H.
  • Pouplier, Hoole, Cederbaum, Greven, Pastätter (2014): Perceptual and articulatory factors in German fricative assimilation. Proceedings of the 10th International Seminar on Speech Production, Cologne, Germany, 5-8 May 2014.
  • Brockhaus S, Scheipl F, Torsten H and Greven S (2014): The Functional Linear Array Model and an Application to Viscosity Curves. Proceedings of the 29th International Workshop on Statistical Modelling, 63–68. Eds.: Kneib T, Sobotka F, Fahrenholz J and Irmer H.
  • Scheipl F, Staicu A-M and Greven S (2014): Functional Additive Mixed Models. Proceedings of the 29th International Workshop on Statistical Modelling, 325–330. Eds.: Kneib T, Sobotka F, Fahrenholz J and Irmer H.
  • Scheipl F, Staicu A-M and Greven S (2014): Functional Additive Mixed Models. Contributions in infinite-dimensional statistics and related topics, 257-262. Eds.: Bongiorno EG, Salinelli E, Goia A, Vieu P. Societa Editrice Esculapio
  • Cederbaum J, Greven S (2013): Functional Linear Mixed Models for Sparsely and Irregularly Sampled Data. Proceedings of the 28th International Workshop on Statistical Modelling, 75–80. Eds.: Muggeo VMR, Capursi V, Boscaino G, Lovison G.
  • Greven S, Crainiceanu C, Caffo B and Reich D (2011): Longitudinal Functional Principal Component Analysis. In: Recent Advances in Functional Data Analysis and Related Topics, 149-154. Ed.: Ferraty F. Physica-Verlag.

    • Current Preprints / Technical Reports / Submitted

  • Köhler, M, Umlauf, N, Beyerlein, A, Winkler, C, Ziegler, AG and Greven S: Flexible Bayesian additive joint models with an application to type 1 diabetes research. arXiv.
  • Cederbaum J, Scheipl F and Greven S: Fast symmetric additive covariance smoothing. arXiv.
  • Rügamer D, Brockhaus S, Gentsch K, Scherer K and Greven S: Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals. arXiv.
  • Brockhaus S, Fuest A, Mayr A, Greven S: Signal Regression Models for Location, Scale and Shape with an Application to Stock Returns. arXiv.
  • Pouplier M, Cederbaum J, Hoole P, Greven S: Mixed modeling for functional data for research in the speech sciences.
  • Bender A, Scheipl F and Kuechenhoff, H: Modeling Exposure-Lag-Response Associations with Penalized Piece-wise Exponential Models. Department of Statistics: Technical Reports, Nr. 192.
  • McLean MW, Scheipl F, Hooker G, Greven S and Ruppert D: Bayesian Functional Generalized Additive Models with Sparsely Observed Covariates. arXiv.

    • Software

  • sparseFLMM(): functional linear mixed models for irregularly or sparsely sampled data using functional principal component analysis, see R package sparseFLMM
  • pffr(): additive regression of functional measurements with functional and/or scalar covariates, included in R package refund
  • FDboost: regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, fitted by a component-wise gradient boosting algorithm, see R package FDboost
  • funData: An S4 Class for Functional Data, provides S4 classes for univariate and multivariate functional data, covering densely or sparsely observed functional data as well as images. R package funData
  • MFPCA: Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional, e.g. functions and images. R package MFPCA
  • cAIC4: Conditional Akaike information criterion for lme4. R package cAIC4


    • We collaborate with the S.M.A.R.T. group (Statistical Methods and Applications for Research in Technology) at Johns Hopkins Univerisity.
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