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A generalized maximum entropy estimator to simple linear measurement error model with a composite indicator

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Abstract

We extend the simple linear measurement error model through the inclusion of a composite indicator by using the generalized maximum entropy estimator. A Monte Carlo simulation study is proposed for comparing the performances of the proposed estimator to his counterpart the ordinary least squares “Adjusted for attenuation”. The two estimators are compared in term of correlation with the true latent variable, standard error and root mean of squared error. Two illustrative case studies are reported in order to discuss the results obtained on the real data set, and relate them to the conclusions drawn via simulation study.

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Notes

  1. In the ESM Appendix is reported the GAMS code.

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Authors and Affiliations

  1. Department of Economics and Management, University of Brescia, Brescia, Italy

    Maurizio Carpita

  2. Department of History, Society and Human Studies, University of Salento, Lecce, Italy

    Enrico Ciavolino

Authors
  1. Maurizio Carpita
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  2. Enrico Ciavolino
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Corresponding author

Correspondence to Maurizio Carpita.

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Carpita, M., Ciavolino, E. A generalized maximum entropy estimator to simple linear measurement error model with a composite indicator. Adv Data Anal Classif 11, 139–158 (2017). https://doi.org/10.1007/s11634-016-0237-y

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  • DOI: https://doi.org/10.1007/s11634-016-0237-y

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