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Department of Statistics
Der Campus Nord ist abgebildet. Im Zentrum des Bildes der Mathetower. © Roland Baege​/​TU Dortmund

Completion of a DFG-projects

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Completion of the DFG-projects "Projective item response theory models for count data and their application as interpretable approximations to black-box machine learning models"

The DFG-project "Projective item response theory models for count data and their application as interpretable approximations to black-box machine learning models" has been completed. Selected publications from this project:

  • Beisemann, M. (2022). A flexible approach to modelling over‐, under‐and equidispersed count data in IRT: The Two‐Parameter Conway–Maxwell–Poisson Model. British Journal of Mathematical and Statistical Psychology, 75(3), 411-443. https://doi.org/10.1111/bmsp.12273
  • Beisemann M, Holling H, Doebler P. (2025). Every Trait Counts: Marginal Maximum Likelihood Estimation for Novel Multidimensional Count Data Item Response Models with Rotation or l1–Regularization for Simple Structure. Psychometrika. 90(1), 304-330. https://doi.org/10.1017/psy.2024.17