Refereed Papers - Statistical Methodology
- Lasser, J., Manik, D., Silbersdorff, A., Säfken, B. and Kneib, T. (2020)
Introductory data science across disciplines, using Python, case studies and industry consulting projects
Teaching Statistics, to appear. - Klein, N., Hothorn, T. Barbanti, L. and Kneib, T. (2020)
Multivariate Conditional Transformation Models
Scandinavian Journal of Statistics, to appear. - Voncken, L., Kneib, T., Albers, C. J., Umlauf, N. and Timmerman, M. E. (2020)
Bayesian Gaussian distributional regression models for more efficient norm estimation
British Journal of Mathematical and Statistical Psychology, to appear. - Klein, N, Carlan, M., Kneib, T., Lang, S. and Wagner, H. (2020)
Bayesian Effect Selection in Structured Additive Distributional Regression Models
Bayesian Analysis, to appear - Marques, I., Klein,. N. and Kneib, T. (2020)
Non-Stationary Spatial Regression for Modelling Monthly Precipitation in Germany
Spatial Statistics, to appear - Briseño Sanchez, G., Hohberg, M., Groll, A. and Kneib, T. (2020)
Flexible instrumental variable distributional regression
Journal of the Royal Statistical Society, Series A (Statistics in Society), 183, 1553-1574 - van der Wurp, H., Groll, A., Kneib, T., Marra, G. and Radice, R. (2020)
Generalised joint regression for count data: a penalty extension for competitive settings
Statistics and Computing, 30, 1419-1432 - Santos, B. and Kneib, T. (2020)
Noncrossing structured additive multiple-output Bayesian quantile regression models
Statistics and Computing, 30, 855-869. - Säfken, B. and Kneib, T. (2020)
Conditional Covariance Penalties for Mixed Models
Scandinavian Journal of Statistics, 47, 990-1010 - Klein, N., Malzahn, D., Rosenberger, A., Lozano-Kühne, J., Kneib, T. and Bickeböller, H. (2020)
Candidate gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios
Journal of Applied Statistics, 47, 2066-2080 - Spiegel, E., Kneib, T. and Otto-Sobotka, F. (2020)
Spatio-Temporal Expectile Regression Models
Statistical Modelling, 20, 386-409 - Michaelis, P., Klein, N. and Kneib, T. (2020)
Mixed Discrete-Continuous Regression - A Novel Approach Based on Weight Functions
Stat, 9(1), e277 - Röder, J., Muntermann, J. and Kneib, T. (2020)
Towards a Taxonomy of Data Heterogeneity
Proceedings of Internationale Tagung Wirtschaftsinformatik, Potsdam, Germany. - Hohberg, M., Pütz, P. and Kneib, T. (2020)
Treatment Effects Beyond the Mean: A Practical Guide Using Distributional Regression
PloS ONE, 15(2): e0226514. . - Klein, N. and Kneib, T. (2020)
Directional Bivariate Quantiles - A Robust Approach based on the Cumulative Distribution Function
AStA Advances in Statistical Analysis, 104, 225-260 - Klein, N., Herwartz, H. and Kneib, T. (2020)
Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales
Journal of Econometrics, 214, 513-539 - Pollice, A., Lasinio, G. J., Rossi, R., Amato, M., Kneib, T. and Lang, S. (2019)
Bayesian Measurement Error Correction in Structured Additive Distributional Regression with an Application to the Analysis of Sensor Data on Soil-Plant Variability
Stochastic Environmental Research and Risk Assessment, 33, 747-763 - Groll, A., Hambuckers, J., Kneib, T. and Umlauf, N. (2019)
LASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape
Computational Statistics & Data Analysis, 140, 59-74. - Martini, J. W. R., Rosales, F., Ha, N.-T., Kneib, T., Heise, J. and Wimmer, V. (2019)
Lost in Translation: On the Problem of Data Coding in Penalized Whole Genome Regression with Interactions
G3 - Genes, Genomes, Genetics, 9, 1117-1129 - Thaden, H., Klein, N. and Kneib, T. (2019)
Multivariate Effect Priors in Semiparametric Recursive Bivariate Gaussian Models
Computational Statistics and Data Analysis, 137, 51-66. - Sobotka , F., Salvati, N., Ranallo, M. G. and Kneib, T. (2019)
Adaptive Semiparametric M-Quantile Regression
Econometrics and Statistics, 11, 116-129. - Kneib, T., Klein, N., Lang, S. and Umlauf, N. (2019)
Modular Regression - A Lego System for Building Structured Additive Distributional Regression Models with Tensor Product Interactions (with discussion and rejoinder)
TEST, 28, 1-59. - Spiegel, E., Kneib, T. and Sobotka, F. (2019)
Generalized Additive Models with Flexible Response Functions
Statistics and Computing, 29, 123-138. - Klein, N., Kneib, T., Marra, G., Radice, R., Rokicki, S. and McGovern, M. (2019)
Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes
Statistics in Medicine, 38, 413-436. - Filippou, P., Kneib, T., Marra, G. and Radice, R. (2019)
A Trivariate Additive Regression Model with Arbitrary Link Functions and Varying Correlation Matrix
Journal of Statistical Planning and Inference, 199, 236-248 - Steiner, W., Baumgartner, B., Guhl, D. and Kneib, T. (2019)
Flexible Estimation of Time-Varying Effects from Retail Panel Data
OR Spectrum, 40, 837–873. - Groll, A., Kneib, T., Mayr, A. and Schauberger, G. (2018)
On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016
Journal of Quantitative Analysis of Sports, 14, 65-79 - Hambuckers, J., Groll, A. and Kneib, T. (2018)
Understanding the Economic Determinants of the Severity of Operational Losses: A regularized Generalized Pareto Regression Approach
Journal of Applied Econometrics, 33, 898-935 - Guhl, D., Baumgartner, B., Steiner, W. J. and Kneib, T. (2018)
Estimating Time-Varying Parameters in Brand Choice Models: A Semiparametric Approach
International Journal of Research in Marketing, 35, 394-414. - Hohberg, M., Landau, K., Kneib, T., Klasen, S. and Zucchini, W. (2018)
Vulnerability to poverty revisited: Flexible modeling and better predictive performance
Journal of Economic Inequality, 16, 439-454 - Hambuckers, J., Kneib, T., Langrock, R. and Sohn, A. (2018)
A Markov-switching Generalized Additive Model for Compound Poisson Processes, with Applications to Operational Losses Models
Quantitative Finance, 18, 1679-1698 - Thaden, H. and Kneib, T. (2018)
Structural Equation Models for Dealing with Spatial Confounding
The American Statistician, 72, 239-252 - Michaelis, P., Klein, N. and Kneib, T. (2018)
Bayesian Multivariate Distributional Regression with Skewed Responses and Skewed Random Effects
Journal of Computational and Graphical Statistics, 27, 602-611 - Umlauf, N. and Kneib, T. (2018)
A Primer on Bayesian Distributional Regression
Statistical Modelling, 18, 219-247 - Pütz, P. and Kneib, T. (2018)
A Penalized Spline Estimator For Fixed Effects Panel Data Models
AStA Advances in Statistical Analysis, 102, 145-166 - Friedrichs, S., Manitz, J., Amos, C. I., Risch, A., Chang-Claude, J., Heinrich, J., Kneib, T., Bickeböller, H. and Hofner, B. (2017)
Pathway-Based Kernel Boosting for the Analysis of Data from Genome-Wide Association Studies
Computational and Mathematical Methods in Medicine, Article ID 6742763 - Thaden, H., Pata, M. P., Klein, N., Cadarso Suarez, C. and Kneib, T. (2017)
Integrating Multivariate Conditionally Autoregressive Spatial Priors into Recursive Bivariate Models for Analyzing Environmental Sensitivity of Mussels
Spatial Statistics, 22, 419-433 - Duarte, E., de Sousa, B., Cadarso-Suarez, C., Kneib, T. and Rodrigues, V. (2017)
Exploring risk factors in breast cancer screening program data using structured geoadditive models with high order interaction
Spatial Statistics, 22, 403-418. - Spiegel, E., Sobotka, F. and Kneib, T. (2017)
Model Selection in Semiparametric Expectile Regression
Electronic Journal of Statistics, 11, 3008-3038 - Waldmann, E., Taylor-Robinson D., Klein, N., Kneib T., Pressler T., Schmid, M. and Mayr, A. (2017)
Boosting Joint Models for Longitudinal and Time-to-Event Data.
Biometrical Journal, 59, 1104-1121. - Waldmann, E., Sobotka, F. and Kneib, T. (2017)
Bayesian regularisation in geoadditive expectile regression
Statistics and Computing, 27, 1539-1553. - Manitz, J., Harbering, J., Schmidt, M., Kneib, T., and Schöbel, A. (2017)
Source estimation for propagation processes on complex networks with an application to delays in public transportation systems
Journal of the Royal Statistical Society, Series C (Applied Statistics), 66, 521-536. - Langrock, R., Kneib, T. and Michelot, T. (2017)
Markov-switching generalized additive models
Statistics and Computing, 27, 259-270. - Sennhenn-Reulen, H. and Kneib, T. (2016)
Structured Fusion Lasso Penalised Multi-state Models
Statistics in Medicine, 35, 4637-4659. - Klein, N. and Kneib, T. (2016)
Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression
Bayesian Analysis, 11, 1071-1106. - Klein, N. and Kneib, T. (2016)
Simultaneous Inference in Structured Additive Conditional Copula Regression Models: A Unifying Bayesian Approach
Statistics and Computing, 26, 841-860. - Michelot, T., Langrock, R., Kneib, T. and King, R. (2016)
Maximum penalized likelihood estimation in semiparametric mark-recapture-recovery models
Biometrical Journal, 58, 222-239 - Reulen, H. and Kneib, T. (2016)
Boosting Multistate Models
Lifetime Data Analysis, 22,241-262 - Hofner, B., Kneib, T. and Hothorn, T. (2016)
A Unified Framework of Constrained Regression
Statistics and Computing, 26, 1-14 - Buck, C., Kneib, T., Tkaczick, T., Konstabel, K. and Pigeot, I. (2015)
Assessing Opportunities for Physical Activity in the Built Environment of Children: Interrelation between Kernel Density and Neighborhood Scale
International Journal of Health Geographics, 14: 35 - Sohn, A., Klein,. N. and Kneib, T. (2015)
A Semiparametric Analysis of Conditional Income Distributions
Schmollers Jahrbuch - Journal of Applied Science Studies, 135, 13-22 - Klein, N., Kneib, T., Lang, S. and Sohn, A. (2015)
Bayesian Structured Additive Distributional Regression with an Application to Regional Income Inequality in Germany
Annals of Applied Statistics, 9, 1024-1052. - Langrock, R., Kneib, T., Sohn, A., and DeRuiter, S. L. (2015)
Nonparametric inference in hidden Markov models using P-splines
Biometrics, 71, 520-528 - Schulze Waltrup, L., Sobotka, F., Kneib, T. and Kauermann, G. (2015)
Expectile and Quantile Regression - David and Goliath?
Statistical Modelling, 15, 433-456 - Langrock, R., Michelot, T., Sohn, A. and Kneib, T. (2015)
Semiparametric stochastic volatility modelling using penalized splines
Computational Statistics, 30, 517-537 - Waldmann, E. and Kneib, T. (2015)
Variational Approximations in Geoadditive Latent Gaussian Regression: Mean and Quantile Regression
Statistics and Computing, 25, 1247-1263 - Rodriguez Alvarez, M. X., Lee, D.-J., Kneib, T., Durban, M. and Eilers, P. (2015)
Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm
Statistics and Computing, 25, 941-957 - Konrath, S., Fahrmeir, L. and Kneib, T. (2015)
Bayesian Accelerated Failure Time Models Based on Penalized Mixtures of Gaussians: Regularization and Variable Selection
AStA Advances in Statistical Analysis, 99, 259-280 - Klein, N., Kneib, T., Klasen, S., and Lang, S. (2015)
Bayesian Structured Additive Distributional Regression for Multivariate Responses
Journal of the Royal Statistical Society Series C (Applied Statistics), 64, 569-591 - Waldmann, E. and Kneib, T. (2015)
Bayesian Bivariate Quantile Regression
Statistical Modelling, 15, 326-344 - Klein, N., Kneib, T. and Lang, S. (2015)
Bayesian Generalized Additive Models for Location, Scale and Shape for Zero-Inflated and Overdispersed Count Data
Journal of the American Statistical Association, 110, 405-419. - Helms, H.-J., Benda, N., Zinserling, J., Kneib, T. and Friede, T. (2015)
Spline-based procedures for dose-finding studies with active control
Statistics in Medicine, 34, 232-248, - Wiesenfarth, M., Matías Hisgen, C., Kneib, T. and Cadarso-Suarez, C. (2014)
Bayesian Nonparametric Instrumental Variable Regression based on Penalized Splines and Dirichlet Process Mixtures
Journal of Business and Economic Statistics, 32, 468-482 - Klein, N., Denuit, M., Lang, S. and Kneib, T. (2014)
Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape
Insurance: Mathematics and Economics, 55, 225-249 - Duarte, E., de Sousa, B., Cadarso-Suarez, C., Rodrigues, V. and Kneib, T. (2014)
Structured additive regression (STAR) modeling of age of menarche and the age of menopause in breast cancer screening program
Biometrical Journal, 56, 416-427 - Lang, S., Umlauf, N., Wechselberger, P., Hartgen, K. and Kneib, T. (2014)
Multilevel Structured Additive Regression
Statistics and Computing, 24, 223-238 - Manitz, J., Kneib, T., Schlather, M., Helbing, D., Brockmann, D. (2014)
Origin Detection during food-borne Disease Outbreaks - A case study of the 2011 EHEC/HUS Outbreak in Germany
PLOS Currents: Outbreaks, Apr 1. Edition 1. - Säfken, 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, 1-301 - Hothorn, T., Kneib, T. and Bühlmann, P. (2014)
Conditional Transformation Models
Journal of the Royal Statistical Society, Series B (Statistical Methodology), 76, 3-27 - Hillmann, J., Kneib, T., Köpcke, L., Juarez Paz, L. M. and Kretzberg, J. (2014)
A Bivariate Cumulative Probit Model for the Comparison of Neuronal Encoding Hypotheses
Biometrical Journal, 56, 23-43 - Freytag, S., Manitz, J., Schlather, M., Kneib, T., Amos, C. I., Risch, A., Chang-Claude, J., Heinrich, J. and Bickeböller, H. (2013)
A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies
Human Heredity, 76, 64-75 - Rodríguez Girondo, M., Kneib, T., Cadarso-Suárez, C. and Abu-Assi, E. (2013)
Model Building in Non Proportional Hazard Regression
Statistics in Medicine, 32, 5301-5314. - Kneib, T. (2013)
Beyond Mean Regression (with discussion and rejoinder)
Statistical Modelling, 13, 275-385 - Scheipl, F., Kneib, T. and Fahrmeir, L. (2013)
Penalized Likelihood and Bayesian Function Selection in Regression Models
AStA Advances in Statistical Analysis, 97, 349-385 - Waldmann, E., Kneib, T., Lang, S., Yue, Y. and Flexeder, C. (2013)
Bayesian Semiparametric Additive Quantile Regression
Statistical Modelling, 13, 223-252 - Sobotka, F., Radice, R., Marra, G. and Kneib, T. (2013)
Estimating the relationship of women's education and fertility in Botswana using an instrumental variable approach to semiparametric expectile regression
Journal of the Royal Statistical Society Series C (Applied Statistics), 62, 25-45 - Sobotka, F., Kauermann, G., Schulze-Waltrup, L. and Kneib, T. (2013)
On Confidence Intervals for Geoadditive Expectile Regression
Statistics and Computing, 23, 135-148 - Hofner, B., Hothorn, T. and Kneib, T. (2013)
Variable Selection and Model Choice in Structured Survival Models
Computational Statistics, 28, 1079-1101.
Preliminary version: Department of Statistics, Technical Report No. 43 - Freytag, S., Amos, C. I., Bickeböller, H., Kneib, T. and Schlather, M. (2012)
Novel Kernel for Correcting Significance Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis
Human Heredity, 74, 97-108 - Scheipl, F., Fahrmeir, L. and Kneib, T. (2012)
Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models
Journal of the American Statistical Association, 107, 1518-1532 - Heinzl, F., Kneib, T. and Fahrmeir, L. (2012)
Additive mixed models with Dirichlet process mixture and P-spline priors
Advances in Statistical Analysis, 96, 47-68
Preliminary version: Department of Statistics, Technical Report No. 68 - Hofner, B., Hothorn, T., Schmid, M. and Kneib, T. (2012)
A Framework for Unbiased Model Selection Based on Boosting
Journal of Computational and Graphical Statistics, 20, 956-971.
Preliminary version: Department of Statistics, Technical Report No. 72 - Sobotka, F. and Kneib, T. (2012)
Geoadditive Expectile Regression
Computational Statistics & Data Analysis, 56, Issue 4, 755-767. - Mayr, A., Fenske, N., Hofner, B., Kneib, T. and Schmid, M. (2012)
Generalized additive models for location scale and shape for high-dimensional data - a flexible approach based on boosting
Journal of the Royal Statistical Society Series C (Applied Statistics), 61, 403-427
Preliminary version: Department of Statistics, Technical Report No. 98 - Fenske, N. Kneib, T. and Hothorn, T. (2011)
Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression
Journal of the American Statistical Association, 106, 494-510.
Preliminary version: Department of Statistics, Technical Report No. 52 - Kneib, T., Knauer, F. and Küchenhoff, H. (2011)
A general approach to the analysis of habitat selection
Environmental and Ecological Statistics, 18, 1-25. Early Online Version
Preliminary version: Department of Statistics, Technical Report No. 1 - Hofner, B., Kneib, T., Hartl, W. and Küchenhoff, H. (2011)
Building Cox-Type Structured Hazard Regression Models with Time-Varying Effects
Statistical Modelling, 11, 3-24
Preliminary version: Department of Statistics, Technical Report No. 27 - Kneib, T., Konrath, S. and Fahrmeir, L. (2011)
High-dimensional Structured Additive Regression Models: Bayesian Regularisation, Smoothing and Predictive Performance
Journal of the Royal Statistical Society Series C (Applied Statistics), 60, 51-70.
Preliminary version: Department of Statistics, Technical Report No. 46 - Greven, S. and Kneib, T. (2010)
On the Behavior of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models
Biometrika, 97, 773-789.
Preliminary version - Krivobokova, T., Kneib, T. and Claeskens, G. (2010)
Simultaneous Confidence Bands for Penalized Spline Estimators
Journal of the American Statistical Association, 105, 852-863.
Preliminary version - Wiesenfarth, M. and Kneib, T. (2010)
Bayesian Geoadditive Sample Selection Models
Journal of the Royal Statistical Society Series C (Applied Statistics), 59, 381-404. - Cadarso-Suarez, C., Meira-Machado, L., Kneib, T. and Gude, F. (2010)
Flexible hazard ratio curves for continuous predictors in multi-state models: an application to breast cancer data
Statistical Modelling, 10, 291-314. - Fahrmeir, L., Kneib, T. and Konrath, S. (2010)
Bayesian Regularisation in Structured Additive Regression: A Unifying Perspective on Shrinkage, Smoothing and Predictor Selection
Statistics and Computing, 20, 203-219. - Kneib, T., Hothorn, T. and Tutz, G. (2009)
Variable Selection and Model Choice in Geoadditive Regression
Biometrics, 65, 626-634. Supplementary Material
Preliminary version: Department of Statistics, Technical Report No. 3 - Scheipl, F. and Kneib, T. (2009)
Locally Adaptive Bayesian P-Splines with a Normal-Exponential-Gamma Prior
Computational Statistics and Data Analysis, 53, 3533-3552.
Preliminary version: Department of Statistics, Technical Report 22 - Fahrmeir, L. and Kneib, T. (2009)
Propriety of Postersiors in Structured Additive Regression Models: Theory and Empirical Evidence.
Journal of Statistical Planning and Inference, 139, 843-859.
Preliminary version: Discussion Paper 510, SFB 386 - Kneib, T., and Hennerfeind, A. (2008)
Bayesian Semiparametric Multi-State Models
Statistical Modelling, 8, 169-198.
Preliminary version: Discussion Paper 502, SFB 386. - Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. and Zeileis, A. (2008)
Conditional Variable Importance for Random Forests
BMC Bioinformatics, 9:307.
Preliminary version: Department of Statistics, Technical Report No. 23 - Kneib, T., Müller, J. and Hothorn, T. (2008)
Spatial Smoothing Techniques for the Assessment of Habitat Suitability
Environmental and Ecological Statistics, 15, 343-364.
Preliminary version: Discussion Paper 492, SFB 386. - Kneib, T., Baumgartner, B. and Steiner, W. J. (2007)
Semiparametric Multinomial Logit Models for Analysing Consumer Choice Behaviour
AStA Advances in Statistical Analysis, 91, 225-244.
Preliminary version: SFB Discussion Paper 501 - Kneib, T. and Fahrmeir, L. (2007)
A mixed model approach for geoadditive hazard regression
Scandinavian Journal of Statistics, 34, 207-228
Preliminary version: SFB Discussion Paper 400 - Kneib, T. (2006)
Mixed model-based inference in geoadditive hazard regression for interval censored survival times
Computational Statistics and Data Analysis, 51, 777-792.
Preliminary version: SFB Discussion Paper 447. - Kneib, T. and Fahrmeir, L. (2006)
Structured additive regression for categorical space-time data: A mixed model approach.
Biometrics, 62, 109-118.
Supplementary material: SFB DiscussionPaper 431
Preliminary version: SFB Discussion Paper 377 - Fahrmeir, L., Kneib, T. and Lang, S. (2004)
Penalized structured additive regression for space-time data: a Bayesian perspective.
Statistica Sinica, 14, 731-761.
Preliminary version: SFB Discussion Paper 305