The results show that, with moderate sample size; the GME outperforms the MLE estimators in terms of mean squared error.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, a data constrained generalized maximum entropy (GME) estimator for the general linear measurement error model is proposed.
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Key Findings & Data:Miller A Maximum Entropy Econometrics: Robust Estimation with limited data (Wiley, New York, 1996)), was formulated as a convex mixed-integer nonlinear optimization problem.
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Conclusion & Impact:The results show that, with moderate sample size; the GME outperforms the MLE estimators in terms of mean squared error.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this paper, a data constrained generalized maximum entropy (GME) estimator for the general linear measurement error model is proposed. GME estimation, as developed by (A. Golan, G. Judge and D. Miller A Maximum Entropy Econometrics: Robust Estimation with limited data (Wiley, New York, 1996)), was formulated as a convex mixed-integer nonlinear optimization problem. Shannon entropy measures and its generalization, namely ‘entropy of order r’ by Tsallis and R´enyi are briefly discussed. A Monte Carlo comparison is made with the classical maximum likelihood estimation (MLE) method. The results show that, with moderate sample size; the GME outperforms the MLE estimators in terms of mean squared error.
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Cite This Article
AL-NESSAR, A. D. (2022). An information-theoretic alternative to maximum likelihood estimation method in ultrastructural measurement error model. Hacettepe Journal of Mathematics and Statistics, Cilt: 40 - Sayı: 3, 469–481. https://ajindex.com/articles/530575
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