Professuren für Statistik und Ökonometrie

Refereed Papers - Statistical Methodology

  1. 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, to appear
  2. Spiegel, E., Kneib, T. and Otto-Sobotka, F. (2019)
    Spatio-Temporal Expectile Regression Models
    Statistical Modelling, to appear.
  3. Thaden, H., Klein, N. and Kneib, T. (2019)
    Multivariate Effect Priors in Semiparametric Recursive Bivariate Gaussian Models
    Computational Statistics and Data Analysis, to appear.
  4. Sobotka , F., Salvati, N., Ranallo, M. G. and Kneib, T. (2019)
    Adaptive Semiparametric M-Quantile Regression
    Econometrics and Statistics, to appear.
  5. 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
    TEST, to appear.
  6. Groll, A., Kneib, T., Mayr, A. and Schauberger, G. (2019)
    Who's the Favourite? - A Bivariate Poisson Model for the UEFA European Football Championship 2016
    Journal of Quantitative Analysis of Sports, to appear
  7. Spiegel, E., Kneib, T. and Sobotka, F. (2019)
    Generalized Additive Models with Flexible Response Functions
    Statistics and Computing, to appear.
  8. 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, to appear.
  9. 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
  10. 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.
  11. 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
  12. 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.
  13. Hohberg, M., Landau, K., Kneib, T., Klasen, S. and Zucchini, W. (2018)
    Enhancing predictive performance of vulnerability to poverty estimates
    Journal of Economic Inequality, 16, 439-454
  14. 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
  15. Thaden, H. and Kneib, T. (2018)
    Structural Equation Models for Dealing with Spatial Confounding
    The American Statistician, 72, 239-252
  16. 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
  17. Umlauf, N. and Kneib, T. (2018)
    A Primer on Bayesian Distributional Regression
    Statistical Modelling, 18, 219-247
  18. Pütz, P. and Kneib, T. (2018)
    A Penalized Spline Estimator For Fixed Effects Panel Data Models
    AStA Advances in Statistical Analysis, 102, 145-166
  19. 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
  20. 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
  21. 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.
  22. Spiegel, E., Sobotka, F. and Kneib, T. (2017)
    Model Selection in Semiparametric Expectile Regression
    Electronic Journal of Statistics, 11, 3008-3038
  23. 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.
  24. Waldmann, E., Sobotka, F. and Kneib, T. (2017)
    Bayesian regularisation in geoadditive expectile regression
    Statistics and Computing, 27, 1539-1553.
  25. 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.
  26. Langrock, R., Kneib, T. and Michelot, T. (2017)
    Markov-switching generalized additive models
    Statistics and Computing, 27, 259-270.
  27. Sennhenn-Reulen, H. and Kneib, T. (2016)
    Structured Fusion Lasso Penalised Multi-state Models
    Statistics in Medicine, 35, 4637-4659.
  28. Klein, N. and Kneib, T. (2016)
    Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression
    Bayesian Analysis, 11, 1071-1106.
  29. 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.
  30. 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
  31. Reulen, H. and Kneib, T. (2016)
    Boosting Multistate Models
    Lifetime Data Analysis, 22,241-262
  32. Hofner, B., Kneib, T. and Hothorn, T. (2016)
    A Unified Framework of Constrained Regression
    Statistics and Computing, 26, 1-14
  33. 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
  34. 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
  35. 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.
  36. Langrock, R., Kneib, T., Sohn, A., and DeRuiter, S. L. (2015)
    Nonparametric inference in hidden Markov models using P-splines
    Biometrics, 71, 520-528
  37. Schulze Waltrup, L., Sobotka, F., Kneib, T. and Kauermann, G. (2015)
    Expectile and Quantile Regression - David and Goliath?
    Statistical Modelling, 15, 433-456
  38. Langrock, R., Michelot, T., Sohn, A. and Kneib, T. (2015)
    Semiparametric stochastic volatility modelling using penalized splines
    Computational Statistics, 30, 517-537
  39. Waldmann, E. and Kneib, T. (2015)
    Variational Approximations in Geoadditive Latent Gaussian Regression: Mean and Quantile Regression
    Statistics and Computing, 25, 1247-1263
  40. 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
  41. 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
  42. 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
  43. Waldmann, E. and Kneib, T. (2015)
    Bayesian Bivariate Quantile Regression
    Statistical Modelling, 15, 326-344
  44. 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.
  45. 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,
  46. 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
  47. 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
  48. 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
  49. Lang, S., Umlauf, N., Wechselberger, P., Hartgen, K. and Kneib, T. (2014)
    Multilevel Structured Additive Regression
    Statistics and Computing, 24, 223-238
  50. 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.
  51. 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
  52. Hothorn, T., Kneib, T. and Bühlmann, P. (2014)
    Conditional Transformation Models
    Journal of the Royal Statistical Society, Series B, 76, 3-27
  53. 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
  54. 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
  55. 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.
  56. Kneib, T. (2013)
    Beyond Mean Regression (with discussion and rejoinder)
    Statistical Modelling, 13, 275-385
  57. 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
  58. Waldmann, E., Kneib, T., Lang, S., Yue, Y. and Flexeder, C. (2013)
    Bayesian Semiparametric Additive Quantile Regression
    Statistical Modelling, 13, 223-252
  59. 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
  60. Sobotka, F., Kauermann, G., Schulze-Waltrup, L. and Kneib, T. (2013)
    On Confidence Intervals for Geoadditive Expectile Regression
    Statistics and Computing, 23, 135-148
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. Sobotka, F. and Kneib, T. (2012)
    Geoadditive Expectile Regression
    Computational Statistics & Data Analysis, 56, Issue 4, 755-767.
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. 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
  73. 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
  74. Wiesenfarth, M. and Kneib, T. (2010)
    Bayesian Geoadditive Sample Selection Models
    Journal of the Royal Statistical Society Series C (Applied Statistics), 59, 381-404.
  75. 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.
  76. 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.
  77. 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
  78. 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
  79. 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
  80. Kneib, T., and Hennerfeind, A. (2008)
    Bayesian Semiparametric Multi-State Models
    Statistical Modelling, 8, 169-198.
    Preliminary version: Discussion Paper 502, SFB 386.
  81. 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
  82. 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.
  83. 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
  84. 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
  85. 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.
  86. 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
  87. 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