Refereed Papers - Statistical Methodology

1. 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.
2. Klein, N., Hothorn, T. Barbanti, L. and Kneib, T. (2020)
   Multivariate Conditional Transformation Models
   Scandinavian Journal of Statistics, to appear.
3. 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.
4. 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
5. Marques, I., Klein,. N. and Kneib, T. (2020)
   Non-Stationary Spatial Regression for Modelling Monthly Precipitation in Germany
   Spatial Statistics, to appear
6. 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
7. 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
8. Santos, B. and Kneib, T. (2020)
   Noncrossing structured additive multiple-output Bayesian quantile regression models
   Statistics and Computing, 30, 855-869.
9. Säfken, B. and Kneib, T. (2020)
   Conditional Covariance Penalties for Mixed Models
   Scandinavian Journal of Statistics, 47, 990-1010
10. 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
11. Spiegel, E., Kneib, T. and Otto-Sobotka, F. (2020)
    Spatio-Temporal Expectile Regression Models
    Statistical Modelling, 20, 386-409
12. Michaelis, P., Klein, N. and Kneib, T. (2020)
    Mixed Discrete-Continuous Regression - A Novel Approach Based on Weight Functions
    Stat, 9(1), e277
13. Röder, J., Muntermann, J. and Kneib, T. (2020)
    Towards a Taxonomy of Data Heterogeneity
    Proceedings of Internationale Tagung Wirtschaftsinformatik, Potsdam, Germany.
14. Hohberg, M., Pütz, P. and Kneib, T. (2020)
    Treatment Effects Beyond the Mean: A Practical Guide Using Distributional Regression
    PloS ONE, 15(2): e0226514. .
15. 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
16. 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
17. 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
18. 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.
19. 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
20. 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.
21. Sobotka , F., Salvati, N., Ranallo, M. G. and Kneib, T. (2019)
    Adaptive Semiparametric M-Quantile Regression
    Econometrics and Statistics, 11, 116-129.
22. 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.
23. Spiegel, E., Kneib, T. and Sobotka, F. (2019)
    Generalized Additive Models with Flexible Response Functions
    Statistics and Computing, 29, 123-138.
24. 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.
25. 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
26. 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.
27. 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
28. 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
29. 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.
30. 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
31. 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
32. Thaden, H. and Kneib, T. (2018)
    Structural Equation Models for Dealing with Spatial Confounding
    The American Statistician, 72, 239-252
33. 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
34. Umlauf, N. and Kneib, T. (2018)
    A Primer on Bayesian Distributional Regression
    Statistical Modelling, 18, 219-247
35. Pütz, P. and Kneib, T. (2018)
    A Penalized Spline Estimator For Fixed Effects Panel Data Models
    AStA Advances in Statistical Analysis, 102, 145-166
36. 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
37. 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
38. 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.
39. Spiegel, E., Sobotka, F. and Kneib, T. (2017)
    Model Selection in Semiparametric Expectile Regression
    Electronic Journal of Statistics, 11, 3008-3038
40. 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.
41. Waldmann, E., Sobotka, F. and Kneib, T. (2017)
    Bayesian regularisation in geoadditive expectile regression
    Statistics and Computing, 27, 1539-1553.
42. 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.
43. Langrock, R., Kneib, T. and Michelot, T. (2017)
    Markov-switching generalized additive models
    Statistics and Computing, 27, 259-270.
44. Sennhenn-Reulen, H. and Kneib, T. (2016)
    Structured Fusion Lasso Penalised Multi-state Models
    Statistics in Medicine, 35, 4637-4659.
45. Klein, N. and Kneib, T. (2016)
    Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression
    Bayesian Analysis, 11, 1071-1106.
46. 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.
47. 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
48. Reulen, H. and Kneib, T. (2016)
    Boosting Multistate Models
    Lifetime Data Analysis, 22,241-262
49. Hofner, B., Kneib, T. and Hothorn, T. (2016)
    A Unified Framework of Constrained Regression
    Statistics and Computing, 26, 1-14
50. 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
51. 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
52. 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.
53. Langrock, R., Kneib, T., Sohn, A., and DeRuiter, S. L. (2015)
    Nonparametric inference in hidden Markov models using P-splines
    Biometrics, 71, 520-528
54. Schulze Waltrup, L., Sobotka, F., Kneib, T. and Kauermann, G. (2015)
    Expectile and Quantile Regression - David and Goliath?
    Statistical Modelling, 15, 433-456
55. Langrock, R., Michelot, T., Sohn, A. and Kneib, T. (2015)
    Semiparametric stochastic volatility modelling using penalized splines
    Computational Statistics, 30, 517-537
56. Waldmann, E. and Kneib, T. (2015)
    Variational Approximations in Geoadditive Latent Gaussian Regression: Mean and Quantile Regression
    Statistics and Computing, 25, 1247-1263
57. 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
58. 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
59. 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
60. Waldmann, E. and Kneib, T. (2015)
    Bayesian Bivariate Quantile Regression
    Statistical Modelling, 15, 326-344
61. 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.
62. 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,
63. 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
64. 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
65. 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
66. Lang, S., Umlauf, N., Wechselberger, P., Hartgen, K. and Kneib, T. (2014)
    Multilevel Structured Additive Regression
    Statistics and Computing, 24, 223-238
67. 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.
68. 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
69. Hothorn, T., Kneib, T. and Bühlmann, P. (2014)
    Conditional Transformation Models
    Journal of the Royal Statistical Society, Series B (Statistical Methodology), 76, 3-27
70. 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
71. 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
72. 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.
73. Kneib, T. (2013)
    Beyond Mean Regression (with discussion and rejoinder)
    Statistical Modelling, 13, 275-385
74. 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
75. Waldmann, E., Kneib, T., Lang, S., Yue, Y. and Flexeder, C. (2013)
    Bayesian Semiparametric Additive Quantile Regression
    Statistical Modelling, 13, 223-252
76. 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
77. Sobotka, F., Kauermann, G., Schulze-Waltrup, L. and Kneib, T. (2013)
    On Confidence Intervals for Geoadditive Expectile Regression
    Statistics and Computing, 23, 135-148
78. 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
79. 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
80. 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
81. 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
82. 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
83. Sobotka, F. and Kneib, T. (2012)
    Geoadditive Expectile Regression
    Computational Statistics & Data Analysis, 56, Issue 4, 755-767.
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. 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
91. Wiesenfarth, M. and Kneib, T. (2010)
    Bayesian Geoadditive Sample Selection Models
    Journal of the Royal Statistical Society Series C (Applied Statistics), 59, 381-404.
92. 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.
93. 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.
94. 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
95. 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
96. 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
97. Kneib, T., and Hennerfeind, A. (2008)
    Bayesian Semiparametric Multi-State Models
    Statistical Modelling, 8, 169-198.
    Preliminary version: Discussion Paper 502, SFB 386.
98. 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
99. 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.
100. 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
101. 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
102. 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.
103. 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
104. 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