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AG Biostatistik

Unsere Gruppe wurde von 2010-2016 im Emmy Noether Programm der Deutschen Forschungsgemeinschaft (DFG) als Emmy Noether-Nachwuchsgruppe "Statistical Methods for Longitudinal Functional Data" gefördert. Publikationen, die aus dem Projekt entstanden, finden sich hier.

Publikationen seit 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, angenommen.
  • 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, angenommen. 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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