the data-constrained generalized maximum entropy estimator of the glm: asymptotic theory and inference

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ID: 240927
2013
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Abstract
Maximum entropy methods of parameter estimation are appealing because they impose no additional structure on the data, other than that explicitly assumed by the analyst. In this paper we prove that the data constrained GME estimator of the general linear model is consistent and asymptotically normal. The approach we take in establishing the asymptotic properties concomitantly identifies a new computationally efficient method for calculating GME estimates. Formulae are developed to compute asymptotic variances and to perform Wald, likelihood ratio, and Lagrangian multiplier statistical tests on model parameters. Monte Carlo simulations are provided to assess the performance of the GME estimator in both large and small sample situations. Furthermore, we extend our results to maximum cross-entropy estimators and indicate a variant of the GME estimator that is unbiased. Finally, we discuss the relationship of GME estimators to Bayesian estimators, pointing out the conditions under which an unbiased GME estimator would be efficient.
Reference Key
cardell2013entropythe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Nicholas Scott Cardell;Ron Mittelhammer;Thomas L. Marsh
Journal European journal of medicinal chemistry
Year 2013
DOI
10.3390/e15051756
URL
http://www.mdpi.com/1099-4300/15/5/1756
Keywords Keywords not found

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