Quantifying alternative splicing from paired-end RNA-sequencing data D Rossell, CSO Attolini, M Kroiss, A Stöcker The annals of applied statistics 8 (1), 309, 2014 | 54 | 2014 |
FDboost: Boosting functional regression models S Brockhaus, D Rügamer, A Stöcker R package version 0.2-0, URL https://CRAN. R-project. org/package= FDboost, 2016 | 30 | 2016 |
Multivariate functional additive mixed models A Volkmann, A Stöcker, F Scheipl, S Greven Statistical Modelling 23 (4), 303-326, 2023 | 21 | 2023 |
Elastic analysis of irregularly or sparsely sampled curves L Steyer, A Stöcker, S Greven Biometrics 79 (3), 2103-2115, 2023 | 19 | 2023 |
The effect of rapid relative humidity changes on fast filter-based aerosol-particle light-absorption measurements: uncertainties and correction schemes S Düsing, B Wehner, T Müller, A Stöcker, A Wiedensohler Atmospheric Measurement Techniques 12 (11), 5879-5895, 2019 | 19 | 2019 |
Pedestrian exposure to black carbon and PM2.5 emissions in urban hot spots: new findings using mobile measurement techniques and flexible Bayesian … HD Alas, A Stöcker, N Umlauf, O Senaweera, S Pfeifer, S Greven, ... Journal of exposure science & environmental epidemiology 32 (4), 604-614, 2022 | 15 | 2022 |
Functional additive regression on shape and form manifolds of planar curves A Stöcker, S Greven arXiv preprint arXiv:2109.02624, 2021 | 12* | 2021 |
Additive density-on-scalar regression in Bayes Hilbert spaces with an application to gender economics EM Maier, A Stöcker, B Fitzenberger, S Greven arXiv preprint arXiv:2110.11771, 2021 | 11 | 2021 |
Boosting functional response models for location, scale and shape with an application to bacterial competition A Stöcker, S Brockhaus, SA Schaffer, B Bronk, M Opitz, S Greven Statistical Modelling 21 (5), 385-404, 2021 | 11 | 2021 |
sparseFLMM: Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data J Cederbaum, A Volkmann, A Stöcker R package version 0.3. 0, URL https://CRAN. R-project. org/package= sparseFLMM, 2019 | 6 | 2019 |
Elastic full Procrustes analysis of plane curves via Hermitian covariance smoothing A Stöcker, M Pfeuffer, L Steyer, S Greven arXiv preprint arXiv:2203.10522, 2022 | 5 | 2022 |
A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction C Berninger, A Stöcker, D Rügamer Journal of Forecasting 41 (1), 181-200, 2022 | 5 | 2022 |
Regression in quotient metric spaces with a focus on elastic curves L Steyer, A Stöcker, S Greven arXiv preprint arXiv:2305.02075, 2023 | 4 | 2023 |
Package ‘FDboost’ S Brockhaus, D Ruegamer, A Stoecker, T Hothorn | 3 | 2018 |
A Functional Extension of Semi-Structured Networks D Rügamer, B Liew, Z Altai, A Stöcker Advances in Neural Information Processing Systems 37, 129888-129913, 2025 | 1 | 2025 |
Comments on: shape-based functional data analysis A Stöcker, L Steyer, S Greven TEST 33 (1), 48-58, 2024 | | 2024 |
Flexible regression for functional object data: curves, shapes and densities A Stöcker lmu, 2022 | | 2022 |
Corrigendum: Quantifying alternative splicing from paired-end RNA-seq data D Rossell, C Stephan-Otto Attolini, M Kroiss, A Stöcker | | 2015 |
A. Online Supplement for Paper I “Elastic Analysis of Irregularly or Sparsely Sampled Curves” L Steyer, A Stöcker, S Greven Statistical Methods for Sparse Functional Object Data: Elastic Curves …, 0 | | |
Elastic Shape Regression for Plane Curves A Stöcker, L Steyer, S Greven Flexible Regression for Functional Object Data: Curves, Shapes and Densities …, 0 | | |