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Adjusted Least Square Estimation for Noisy Images

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Advances in Multivariate Data Analysis

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Abstract

In this paper we consider the problem of parametric estimation of a linear model corrupted by measurement error. In order to take into account the biasing effects caused by the presence of an external source of error, we propose an adjustment to the least square estimator. The statistical properties of the adjusted estimator are then experimentally verified with respect to two models characterising the literature of images analysis.

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Author information

Authors and Affiliations

  1. Dipartimento di Metodi Quantitativi e Teoria Economica, Facoltà di Scienze Manageriali dell’Università ”G. d’Annunzio” di Chieti-Pescara, 65127, Pescara, Italy

    Ippoliti Luigi

  2. Istituto Nazionale di Statistica, Ufficio regionale per il Molise, Via Mazzini 129, 86100, Campobasso, Italy

    Romagnoli Luca

Authors
  1. Ippoliti Luigi
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  2. Romagnoli Luca
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Editor information

Editors and Affiliations

  1. Institute of Statistics, RWTH Aachen University, Wuellnerstr. 3, 52056, Aachen, Germany

    Hans-Hermann Bock

  2. Department of Statistics and Mathematics Silvio Vianelli, University of Palermo, Viale delle Scienze, 90128, Palermo, Italy

    Marcello Chiodi  & Antonino Mineo  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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Luigi, I., Luca, R. (2004). Adjusted Least Square Estimation for Noisy Images. In: Bock, HH., Chiodi, M., Mineo, A. (eds) Advances in Multivariate Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17111-6_21

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  • DOI: https://doi.org/10.1007/978-3-642-17111-6_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20889-1

  • Online ISBN: 978-3-642-17111-6

  • eBook Packages: Springer Book Archive

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