A weighted generalized maximum entropy estimator with a data-driven weight
type: Item
instance of: scholarly article
title: A weighted generalized maximum entropy estimator with a data-driven weight
zbMATH Open document ID: 1179.91209
author: Ximing Wu
published in: Entropy
publication date: 2010-01-29T00:00:00Z
review text: Summary: The method of Generalized Maximum Entropy (GME), proposed by \textit{A. Golan, G. Judge} and \textit{D. Miller} [Maximum entropy econometrics: robust estimation with limited data. (1996; Zbl 0884.62126)], is an information-theoretic approach that is robust to multicolinearity problem. It uses an objective function that is the sum of the entropies for coefficient distributions and disturbance distributions. This method can be generalized to the weighted GME (W-GME), where different weights are assigned to the two entropies in the objective function. We propose a data-driven method to select the weights in the entropy objective function. We use the least squares cross validation to derive the optimal weights. MonteCarlo simulations demonstrate that the proposedW-GME estimator is comparable to and often outperforms the conventional GME estimator, which places equal weights on the entropies of coefficient and disturbance distributions.
Mathematics Subject Classification ID: 91B80
Mathematics Subject Classification ID: 91G70
Mathematics Subject Classification ID: 62P20
zbMATH DE Number: 5664179
zbMATH Keywords: maximum entropy
zbMATH Keywords: generalized maximum entropy method
zbMATH Keywords: cross validation
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e11040917
OpenAlex ID: W2078530793
cites work: Q4365235
cites work: A Mathematical Theory of Communication
cites work: Information Theory and Statistical Mechanics
cites work: An information theoretical approach to inversion problems
cites work: Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy
cites work: Q3333811
cites work: Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems
cites work: An asymptotically optimal window selection rule for kernel density estimates
cites work: Extent to which least-squares cross-validation minimises integrated square error in nonparametric density estimation
DOI: 10.3390/E11040917
description: scientific article; zbMATH DE number 5664179