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Advances in Data Analysis and Classification

Erschienen in:

17.02.2016 | Regular Article

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

verfasst von: Maurizio Carpita, Enrico Ciavolino

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 1/2017

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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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In the ESM Appendix is reported the GAMS code.

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Titel: A generalized maximum entropy estimator to simple linear measurement error model with a composite indicator
verfasst von: Maurizio Carpita
Enrico Ciavolino
Publikationsdatum: 17.02.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 1/2017
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-016-0237-y

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