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A penalization method to estimate the intrinsic dimensionality of data

Author

Listed:
  • Liliana Forzani

    (Universidad Nacional del Litoral)

  • Daniela Rodriguez

    (Universidad Torcuato Di Tella)

  • Mariela Sued

    (Universidad San Andrés)

Abstract

We propose a novel penalization method for estimating the intrinsic dimensionality of data within a Probabilistic Principal Components Model, extending beyond the Gaussian case. Unlike existing approaches, our method is designed to handle non-normal data, providing a flexible alternative to traditional factor models. Our procedure identifies the dimension at which the eigenvalues of a scatter matrix stabilize. We establish the consistency of the procedure under mild conditions and demonstrate its robustness across a range of data distributions. A comparative analysis highlights its advantages over existing techniques, making it a valuable tool for dimensionality estimation without relying on distributional assumptions.

Suggested Citation

  • Liliana Forzani & Daniela Rodriguez & Mariela Sued, 2025. "A penalization method to estimate the intrinsic dimensionality of data," Statistical Papers, Springer, vol. 66(2), pages 1-20, February.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:2:d:10.1007_s00362-025-01667-0
    DOI: 10.1007/s00362-025-01667-0
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