In this paper, we conduct a set of Monte Carlo sampling experiments to examine the effect of design characteristics on the inequality restricted maximum entropy (RME) estimator. We generate data under varying design characteristics, and estimate the parameters using maximum entropy and least squares estimation, both with and without parameter inequality restrictions. As part of the experimental design we vary the sample size, the distribution of the regressors, the distribution of the errors, the degree of collinearity, the signal-to-noise ratio, and the specification error. We compare the alternative estimators on the basis of mean square error
In density estimation task, Maximum Entropy (Maxent) model can effectively use reliable prior inform...
The estimation of Maximum Entropy (ME) median variance based on odd samples is discussed. The estima...
The maximum entropy characterization of the von Mises distribution on the circle is exploited to der...
In this paper, we conduct a set of Monte Carlo sampling experiments to examine the effect of design ...
Maximum entropy estimation is a relatively new estimation technique in econometrics. We carry out se...
When Shannon entropy is used as a criterion in the optimal design of experiments, advantage can be t...
<p>The entropy difference was used as a divergence measure. Significance level was . Rates were esti...
The focus of this paper is on finding suitable estimators of the entropy for different fixed size pi...
Maximum entropy models have become popular statistical models in neuroscience and other areas in bio...
Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfalls...
Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfall...
Abstract: Consider the linear regression model y = X+ u in the usual notation. In many applications ...
Maximum entropy models have become popular statistical models in neuroscience and other areas in bio...
The claim has been made that the Generalized Maximum Entropy (GME) estimator of Golan, Judge and Mil...
The total entropy utility function is considered for the dual purpose of Bayesian design for model d...
In density estimation task, Maximum Entropy (Maxent) model can effectively use reliable prior inform...
The estimation of Maximum Entropy (ME) median variance based on odd samples is discussed. The estima...
The maximum entropy characterization of the von Mises distribution on the circle is exploited to der...
In this paper, we conduct a set of Monte Carlo sampling experiments to examine the effect of design ...
Maximum entropy estimation is a relatively new estimation technique in econometrics. We carry out se...
When Shannon entropy is used as a criterion in the optimal design of experiments, advantage can be t...
<p>The entropy difference was used as a divergence measure. Significance level was . Rates were esti...
The focus of this paper is on finding suitable estimators of the entropy for different fixed size pi...
Maximum entropy models have become popular statistical models in neuroscience and other areas in bio...
Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfalls...
Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfall...
Abstract: Consider the linear regression model y = X+ u in the usual notation. In many applications ...
Maximum entropy models have become popular statistical models in neuroscience and other areas in bio...
The claim has been made that the Generalized Maximum Entropy (GME) estimator of Golan, Judge and Mil...
The total entropy utility function is considered for the dual purpose of Bayesian design for model d...
In density estimation task, Maximum Entropy (Maxent) model can effectively use reliable prior inform...
The estimation of Maximum Entropy (ME) median variance based on odd samples is discussed. The estima...
The maximum entropy characterization of the von Mises distribution on the circle is exploited to der...