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Penalized Model Selection for Ill-Posed Linear Problems

In: Recent Advances in Applied Probability

Author

Listed:
  • Carenne Ludeña

    (Instituto Venezolano de Investigaciones Científicas)

  • Ricardo Ríos

    (Universidad Central de Venezuela, Facutad de Ciencias. Escuela de Matemáticas)

Abstract

In this article we review the problem of discretization-regularization for inverse linear ill-posed problems from a statistical point of view. We discuss the problem in the context of adaptive model selection and relate these results to Bayesian estimation.

Suggested Citation

  • Carenne Ludeña & Ricardo Ríos, 2005. "Penalized Model Selection for Ill-Posed Linear Problems," Springer Books, in: Ricardo Baeza-Yates & Joseph Glaz & Henryk Gzyl & Jürgen Hüsler & José Luis Palacios (ed.), Recent Advances in Applied Probability, pages 299-327, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-23394-9_14
    DOI: 10.1007/0-387-23394-6_14
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