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Advances in Data Analysis and Classification

, Volume 11, Issue 1, pp 139–158 | Cite as

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

  • Maurizio CarpitaEmail author
  • Enrico Ciavolino
Regular Article

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.

Keywords

Simple linear measurement error model Generalized maximum entropy Composite indicator Global innovation index Manager performance 

Mathematics Subject Classification

97K70 97K80 47N30 94A17 91B82 

Supplementary material

11634_2016_237_MOESM1_ESM.pdf (23 kb)
Supplementary material 1 (pdf 23 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Economics and ManagementUniversity of BresciaBresciaItaly
  2. 2.Department of History, Society and Human StudiesUniversity of SalentoLecceItaly

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