Professuren für Statistik und Ökonometrie

Refereed Papers - Statistical Methodology

  1. Henrich, J., van Delden, J., Seidel, D., Kneib, T and Ecker, A. (2025)
    TreeLearn: A Comprehensive Deep Learning Method for Segmenting Individual Trees from Forest Point Clouds
    Ecological Informatics, to appear
  2. Reuter, A., Thielmann, A., Weisser, C., Säfken, B. and Kneib, T. (2025)
    Probabilistic Topic Modelling with Transformer Representations
    IEEE Transactions on Neural Networks and Learning Systems, to appear.
  3. Rappl, A.,Carlan, M., Kneib, T., Klokman, S., and Bergherr, E. (2025)
    Bayesian Effect Selection in Structured Additive Quantile Regression
    Statistical Modelling, to appear
  4. Säfken, B., Kneib, T. and Wood, S. (2024)
    On the degrees of freedom of the smoothing parameter
    Biometrika, 112, asae052
  5. Thielmann, A., Reuter, A., Kneib, T., Rügamer, D. and Säfken, B. (2024)
    Interpretable Additive Tabular Transformer Networks
    Transactions on Machine Learning Research, 05/2024, 1-19
  6. Urdangarin, A., Goicoa, T., Kneib, T., and Ugarte, M.D. (2024)
    A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models
    Spatial Statistics, 59, 100804
  7. Carlan, M. and Kneib, T. (2024)
    Bayesian Discrete Conditional Transformation Models
    Statistical Modelling, 24, 203-225
  8. Dorn, F., Radice, R., Marra, G. and Kneib, T. (2024)
    A Bivariate Relative Poverty Line for Time and Income Poverty: Detecting Intersectional Differences Using Distributional Copulas
    Review of Income and Wealth, 70, 395-419
  9. Lichter, J., Wiemann, P. and Kneib, T. (2024)
    Variational Inference: Uncertainty Quantification in Additive Models
    AStA Advances in Statistical Analysis, 108, 279-331
  10. Carlan, M., Kneib, T. and Klein, N. (2023)
    Bayesian Conditional Transformation Models
    Journal of the American Statistical Association, 119, 1360-1373
  11. Wiemann, P., Kneib, T. and Hambuckers, J. (2023)
    Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond
    Statistical Papers, 65, 3155-3180
  12. Schmidt, R. and Kneib, T. (2023):
    Multivariate Distributional Stochastic Frontier Models
    Computational Statistics and Data Analysis, 187, 107796
  13. Rappl, A., Kneib, T., Lang, S. and Bergherr, E. (2023)
    Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data
    Statistics and Computing, 33, 135
  14. Riebl, H., Klein, N. and Kneib, T. (2023)
    Modeling Intra-Annual Tree Stem Growth with a Distributional Regression Approach for Gaussian Process Responses
    Journal of the Royal Statistical Society, Series C (Applied Statistics), 72, 414-433
  15. Lado Baleato, Ó., Kneib, T., Cadarso-Suárez, C., Gude, F. (2023)
    Multivariate reference regions based on Multivariate Conditional Transformation Models. Application in the measurement of glycemic markers in diabetes
    Biometrical Journal, 65, 2200229
  16. Marques, I. M., Wiemann, P. F .V. and Kneib, T. (2023)
    A variance partitioning multi-resolution model for forest inventory data with a fixed plot design
    Journal of Agricultural, Biological and Ecological Statistics, 28, 706-725
  17. Kant, G., Weisser, C., Kneib, T., Säfken, B. (2023)
    Topic Model-Machine Learning Classifier Integrations on Geocoded Twitter Data.
    In: Phuong, N.H., Kreinovich, V. (eds) Biomedical and Other Applications of Soft Computing. Studies in Computational Intelligence, vol 1045
    Springer, Cham.
  18. Rügamer, D., Baumann, P., Kneib, T. and Hothorn, T. (2023)
    Probabilistic time series forecasts with autoregressive transformation models
    Statistics and Computing, 33, 37
  19. Thielmann, A., Weißer, C., Kneib, T. and Säfken, B. (2023)
    Coherence based Document Clustering
    17th IEEE International Conference on Semantic Computing (ICSC) 2023, 9-16
  20. Martínez-Flórez, G.,Barrera-Causil, C., Kuang, S., Fazlali, Z., Wegener, D., Kneib, T., De Bastiani, F. and Marmolejo-Ramos, F. (2023)
    Generalised Exponential-Gaussian distribution: a method for neural reaction time analysis
    Cognitive Neurodynamics, 17, 221-237
  21. Thielmann, A., Weisser, C., Gerloff, C., Python, A., Kneib, T. and Säfken, B. (2023)
    Pseudo-Document Simulation for Comparing LDA, GSDMM and GPM Topic Models on Short and Sparse Text using Twitter Data
    Computational Statistics, 38, 647-674
  22. Friedrich, S., Groll, A., Ickstadt, K., Kneib, T., Pauly, M., Rahnenführer, J. and Friede, T. (2023)
    Regularization approaches in clinical biostatistics: A review of methods and their applications
    Statistical Methods in Medical Research, 32, 425-440
  23. Kneib, T., Silbersdorff, A. and Säfken, B. (2023)
    Rage Against the Mean - A Review of Distributional Regression Approaches
    Econometrics and Statistics, 26, 99-123
  24. Marmolejo-Ramos, F., Tejo, M., Brabec, M., Kuzilek, J., Joksimovic, S., Kovanovic, V., González, J., Kneib, T., Bühlmann, P., Kook, L., Briseño-Sánchez, G., Ospina, R. (2023)
    Distributional regression modelling via GAMLSS. An overview through a data set from learning analytics
    WIREs Data Mining and Knowledge Discovery, 13, e1479
  25. Marques, I., Kneib, T. and Klein, N. (2022)
    A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes
    Statistics and Computing, 32, 73.
  26. Stadlmann, S. and Kneib, T. (2022)
    Interactively visualizing distributional regression models with distreg.vis
    Statistical Modelling, 22, 527-545
  27. Wiemann, P., Klein, N. and Kneib, T. (2022)
    Correcting for Sample Selection Bias in Bayesian Distributional Regression Models
    Computational Statistics and Data Analysis, 168, 107382
  28. Martins, R., de Sousa, B., Kneib, T., Hohberg, M., Klein, N., Rodrigues, V. and Duarte, E. (2022)
    Is age at menopause increasing? - Comparison of imputation methods to handle missing values
    BMC Medical Research Methodology, 22, 187
  29. Tillmann, A., Kqiku, L., Reinhardt, D., Weisser, C., Säfken, B. and Kneib, T. (2022)
    Privacy Estimation on Twitter: Modelling the Effect of Latent Topics on Privacy by Integrating XGBoost, Topic and Generalized Additive Models 2022 IEEE Smart World Congress
  30. Marques, I., Kneib, T. and Klein, N.(2022)
    Mitigating Spatial Confounding by Explicitly Correlating Gaussian Random Fields
    Environmetrics, 33, e2727
  31. Hohberg, M., Donat, F., Marra, G. and Kneib, T. (2022)
    Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes
    Journal of the Royal Statistical Society, Series C (Applied Statistics), 70, 1365-1390
  32. Adam, T., Mayr, A. and Kneib, T. (2022)
    Gradient boosting in Markov-switching generalized additive models for location, scale and shape
    Econometrics and Statistics, 22, 3-16
  33. Klein, N., Hothorn, T. Barbanti, L. and Kneib, T. (2022)
    Multivariate Conditional Transformation Models
    Scandinavian Journal of Statistics, 49, 116-142
  34. Hambuckers, J. and Kneib, T. (2021)
    Smooth-transition regression models for non-stationary extremes
    Journal of Financial Econometrics, nbab005
  35. Luber, M., Weisser, C., Säfken, B., Silbersdorff, A., Kneib, T. and Kis-Katos, K. (2021)
    Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment analysis and Structural Break Models for Large Scale Data
    In Bright J., Giachanou A., Spaiser V., Spezzano F., George A., Pavliuc A. (Eds), Disinformation in Open Online Media. MISDOOM 2021. Springer Lecture Notes in Computer Science
  36. Spiegel, E., Kneib, T., von Gablenz, P. and Otto-Sobotka, F. (2021)
    Generalized expectile regression with flexible response function
    Biometrical Journal, 63, 1028-1051
  37. Lasser, J., Manik, D., Silbersdorff, A., Säfken, B. and Kneib, T. (2021)
    Introductory data science across disciplines, using Python, case studies and industry consulting projects
    Teaching Statistics, 43, S190-S200
  38. Voncken, L., Kneib, T., Albers, C. J., Umlauf, N. and Timmerman, M. E. (2021)
    Bayesian Gaussian distributional regression models for more efficient norm estimation
    British Journal of Mathematical and Statistical Psychology, 74, 99-117
  39. Klein, N, Carlan, M., Kneib, T., Lang, S. and Wagner, H. (2021)
    Bayesian Effect Selection in Structured Additive Distributional Regression Models
    Bayesian Analysis, 16, 545-573
  40. Marques, I., Klein,. N. and Kneib, T. (2020)
    Non-Stationary Spatial Regression for Modelling Monthly Precipitation in Germany
    Spatial Statistics, 40, 100386
  41. 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
  42. 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
  43. Santos, B. and Kneib, T. (2020)
    Noncrossing structured additive multiple-output Bayesian quantile regression models
    Statistics and Computing, 30, 855-869.
  44. Säfken, B. and Kneib, T. (2020)
    Conditional Covariance Penalties for Mixed Models
    Scandinavian Journal of Statistics, 47, 990-1010
  45. 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
  46. Spiegel, E., Kneib, T. and Otto-Sobotka, F. (2020)
    Spatio-Temporal Expectile Regression Models
    Statistical Modelling, 20, 386-409
  47. Michaelis, P., Klein, N. and Kneib, T. (2020)
    Mixed Discrete-Continuous Regression - A Novel Approach Based on Weight Functions
    Stat, 9(1), e277
  48. Röder, J., Muntermann, J. and Kneib, T. (2020)
    Towards a Taxonomy of Data Heterogeneity
    Proceedings of Internationale Tagung Wirtschaftsinformatik, Potsdam, Germany.
  49. Hohberg, M., Pütz, P. and Kneib, T. (2020)
    Treatment Effects Beyond the Mean: A Practical Guide Using Distributional Regression
    PloS ONE, 15(2): e0226514. .
  50. 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
  51. 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
  52. 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
  53. 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.
  54. 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
  55. 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.
  56. Sobotka , F., Salvati, N., Ranallo, M. G. and Kneib, T. (2019)
    Adaptive Semiparametric M-Quantile Regression
    Econometrics and Statistics, 11, 116-129.
  57. 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.
  58. Spiegel, E., Kneib, T. and Sobotka, F. (2019)
    Generalized Additive Models with Flexible Response Functions
    Statistics and Computing, 29, 123-138.
  59. 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.
  60. 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
  61. 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.
  62. 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
  63. 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
  64. 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.
  65. 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
  66. 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
  67. Thaden, H. and Kneib, T. (2018)
    Structural Equation Models for Dealing with Spatial Confounding
    The American Statistician, 72, 239-252
  68. 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
  69. Umlauf, N. and Kneib, T. (2018)
    A Primer on Bayesian Distributional Regression
    Statistical Modelling, 18, 219-247
  70. Pütz, P. and Kneib, T. (2018)
    A Penalized Spline Estimator For Fixed Effects Panel Data Models
    AStA Advances in Statistical Analysis, 102, 145-166
  71. 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
  72. 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
  73. 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.
  74. Spiegel, E., Sobotka, F. and Kneib, T. (2017)
    Model Selection in Semiparametric Expectile Regression
    Electronic Journal of Statistics, 11, 3008-3038
  75. 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.
  76. Waldmann, E., Sobotka, F. and Kneib, T. (2017)
    Bayesian regularisation in geoadditive expectile regression
    Statistics and Computing, 27, 1539-1553.
  77. 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.
  78. Langrock, R., Kneib, T. and Michelot, T. (2017)
    Markov-switching generalized additive models
    Statistics and Computing, 27, 259-270.
  79. Sennhenn-Reulen, H. and Kneib, T. (2016)
    Structured Fusion Lasso Penalised Multi-state Models
    Statistics in Medicine, 35, 4637-4659.
  80. Klein, N. and Kneib, T. (2016)
    Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression
    Bayesian Analysis, 11, 1071-1106.
  81. 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.
  82. 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
  83. Reulen, H. and Kneib, T. (2016)
    Boosting Multistate Models
    Lifetime Data Analysis, 22,241-262
  84. Hofner, B., Kneib, T. and Hothorn, T. (2016)
    A Unified Framework of Constrained Regression
    Statistics and Computing, 26, 1-14
  85. 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
  86. 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
  87. 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.
  88. Langrock, R., Kneib, T., Sohn, A., and DeRuiter, S. L. (2015)
    Nonparametric inference in hidden Markov models using P-splines
    Biometrics, 71, 520-528
  89. Schulze Waltrup, L., Sobotka, F., Kneib, T. and Kauermann, G. (2015)
    Expectile and Quantile Regression - David and Goliath?
    Statistical Modelling, 15, 433-456
  90. Langrock, R., Michelot, T., Sohn, A. and Kneib, T. (2015)
    Semiparametric stochastic volatility modelling using penalized splines
    Computational Statistics, 30, 517-537
  91. Waldmann, E. and Kneib, T. (2015)
    Variational Approximations in Geoadditive Latent Gaussian Regression: Mean and Quantile Regression
    Statistics and Computing, 25, 1247-1263
  92. 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
  93. 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
  94. 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
  95. Waldmann, E. and Kneib, T. (2015)
    Bayesian Bivariate Quantile Regression
    Statistical Modelling, 15, 326-344
  96. 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.
  97. 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,
  98. 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
  99. 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
  100. 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
  101. Lang, S., Umlauf, N., Wechselberger, P., Hartgen, K. and Kneib, T. (2014)
    Multilevel Structured Additive Regression
    Statistics and Computing, 24, 223-238
  102. 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.
  103. 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
  104. Hothorn, T., Kneib, T. and Bühlmann, P. (2014)
    Conditional Transformation Models
    Journal of the Royal Statistical Society, Series B (Statistical Methodology), 76, 3-27
  105. 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
  106. 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
  107. 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.
  108. Kneib, T. (2013)
    Beyond Mean Regression (with discussion and rejoinder)
    Statistical Modelling, 13, 275-385
  109. 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
  110. Waldmann, E., Kneib, T., Lang, S., Yue, Y. and Flexeder, C. (2013)
    Bayesian Semiparametric Additive Quantile Regression
    Statistical Modelling, 13, 223-252
  111. 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
  112. Sobotka, F., Kauermann, G., Schulze-Waltrup, L. and Kneib, T. (2013)
    On Confidence Intervals for Geoadditive Expectile Regression
    Statistics and Computing, 23, 135-148
  113. 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
  114. 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
  115. 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
  116. 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
  117. 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
  118. Sobotka, F. and Kneib, T. (2012)
    Geoadditive Expectile Regression
    Computational Statistics & Data Analysis, 56, Issue 4, 755-767.
  119. 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
  120. 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
  121. 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
  122. 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
  123. 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
  124. 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
  125. 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
  126. Wiesenfarth, M. and Kneib, T. (2010)
    Bayesian Geoadditive Sample Selection Models
    Journal of the Royal Statistical Society Series C (Applied Statistics), 59, 381-404.
  127. 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.
  128. 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.
  129. 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
  130. 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
  131. 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
  132. Kneib, T., and Hennerfeind, A. (2008)
    Bayesian Semiparametric Multi-State Models
    Statistical Modelling, 8, 169-198.
    Preliminary version: Discussion Paper 502, SFB 386.
  133. 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
  134. 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.
  135. 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
  136. 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
  137. 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.
  138. 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
  139. 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