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https://hdl.handle.net/11000/34492
A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
Título : A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria |
Autor : Pérez-Sánchez, Belén  González Espinosa, Martín  Perea, Carmen  López-Espín, Jose J.  |
Editor : MDPI |
Departamento: Departamentos de la UMH::Estadística, Matemáticas e Informática |
Fecha de publicación: 2021 |
URI : https://hdl.handle.net/11000/34492 |
Resumen :
Abstract: Simultaneous Equations Models (SEM) is a statistical technique widely used in economic
science to model the simultaneity relationship between variables. In the past years, this technique
has also been used in other fields such as psychology or medicine. Thus, the development of new
estimating methods is an important line of research. In fact, if we want to apply the SEM to medical
problems with the main goal being to obtain the best approximation between the parameters of
model and their estimations. This paper shows a computational study between different methods for
estimating simultaneous equations models as well as a new method which allows the estimation of
those parameters based on the optimization of the Bayesian Method of Moments and minimizing the
Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter
criteria to compare the estimation methods studied. The comparison between those methods is
performed through an experimental study using randomly generated models. The experimental
study compares the estimations obtained by the different methods as well as the efficiency when
comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the
proposed estimation method offered better approximations and the entropy measured results more
efficiently than the rest.
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Palabras clave/Materias: simultaneous equations models bayesian method of moments markov chain monte carlo akaike information criteria entropy computational statistics |
Área de conocimiento : CDU: Ciencias puras y naturales: Matemáticas |
Tipo de documento : info:eu-repo/semantics/article |
Derechos de acceso: info:eu-repo/semantics/openAccess |
DOI : https://doi.org/10.3390/math9070700 |
Aparece en las colecciones: Artículos Estadística, Matemáticas e Informática
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