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Derivatives of the Log of a Determinant

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Developments in Statistical Modelling (IWSM 2024)

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

We present an efficient way to calculate effective model dimensions, using automated differentiation of the Cholesky algorithm. The method is illustrated with two examples using P-splines: adaptive smoothing and smoothing of over-dispersed counts.

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References

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Author information

Authors and Affiliations

  1. Erasmus MC, Rotterdam, The Netherlands

    Paul H. C. Eilers

  2. Wageningen University and Research, Wageningen, The Netherlands

    Martin P. Boer

Authors
  1. Paul H. C. Eilers
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  2. Martin P. Boer
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Corresponding author

Correspondence to Paul H. C. Eilers .

Editor information

Editors and Affiliations

  1. Department of Mathematical Sciences, Durham University, Durham, UK

    Jochen Einbeck

  2. Department of Mathematical Sciences, Durham University, Durham, UK

    Hyeyoung Maeng

  3. Department of Mathematical Sciences, Durham University, Durham, UK

    Emmanuel Ogundimu

  4. Department of Mathematical Sciences, Durham University, Durham, UK

    Konstantinos Perrakis

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Eilers, P.H.C., Boer, M.P. (2024). Derivatives of the Log of a Determinant. In: Einbeck, J., Maeng, H., Ogundimu, E., Perrakis, K. (eds) Developments in Statistical Modelling. IWSM 2024. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-65723-8_11

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