Discussion of the Paper "Connecting Model-Based and Model-Free Approaches to Linear Least Squares Regression" by Lutz Dümbgen and Laurie Davies (2024)

Authors

  • Moritz Herrmann LMU Munich, Munich Center for Machine Learning, Germany
  • Michael Herrmann Eberhard Karls University Tübingen, Germany

DOI:

https://doi.org/10.60923/issn.1973-2201/22203

References

L. BREIMAN (2001). Statistical modeling: the two cultures (with comments and a rejoinder by the author). Statistical Science, 16, no. 3, pp. 199–231.

P. L. DAVIES (2014). Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis. CRC Press, New York.

P. L. DAVIES (2024). Statistics: truth, ontology, approximation, honesty and indoctrination. Statistica, 84, no. 2, pp. 83–92.

L. DÜMBGEN, L. DAVIES (2024). Connecting model-based and model-free approaches to linear least squares regression. Statistica, 84, no. 2, pp. 65–81.

R. FISHER (1955). Statistical methods and scientific induction. Journal of the Royal Statistical Society: Series B (Methodological), 17, no. 1, pp. 69–78.

P. GOOD (1994). Permutation Tests. Springer Series in Statistics, New York.

S.GREENLAND (2023). Connecting simple and precise P-values to complex and ambiguous realities (includes rejoinder to comments on “Divergence vs. decision P-values”). Scandinavian Journal of Statistics, 50, no. 3, pp. 899–914.

J.W. TUKEY (1993). Issues relevant to an honest account of data-based inference, partially in the light of laurie davies’s paper. Princeton University, Princeton.

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Published

2025-11-25

How to Cite

Herrmann, M., & Herrmann, M. (2024). Discussion of the Paper "Connecting Model-Based and Model-Free Approaches to Linear Least Squares Regression" by Lutz Dümbgen and Laurie Davies (2024). Statistica, 84(2), 97–99. https://doi.org/10.60923/issn.1973-2201/22203