Generalized maximum entropy estimation

A Generalized Maximum Entropy Stochastic Frontier Measuring Productivity Accounting for Spatial Dependency

In this paper, a stochastic frontier model accounting for spatial dependency is developed using generalized maximum entropy estimation. An application is made for measuring total factor productivity in European agriculture. The empirical results show that agricultural productivity growth in Europe is driven by upward movements of technology over time through technological developments. Results ...

Generalized Maximum Entropy estimation method for studying the effect of the Management’s Factors on the Enterprise Performances

An application of general maximum entropy to utility

Methodologies related with information theory have been increasingly used in studies in economics and management. In this paper we use Generalized Maximum Entropy as an alternative to the Ordinary Least Squares in the estimation of utility functions. We estimated linear, logarithmic and power utility functions as well as confidence intervals and in order to compare both methodologies. Results p...

Modeling of the Maximum Entropy Problem as an Optimal Control Problem and its Application to Pdf Estimation of Electricity Price

In this paper, the continuous optimal control theory is used to model and solve the maximum entropy problem for a continuous random variable. The maximum entropy principle provides a method to obtain least-biased probability density function (Pdf) estimation. In this paper, to find a closed form solution for the maximum entropy problem with any number of moment constraints, the entropy is consi...

Duality of Maximum Entropy and Minimum Divergence

We discuss a special class of generalized divergence measures by the use of generator functions. Any divergence measure in the class is separated into the difference between cross and diagonal entropy. The diagonal entropy measure in the class associates with a model of maximum entropy distributions; the divergence measure leads to statistical estimation via minimization, for arbitrarily giving...

Parameter estimation for 3-parameter generalized pareto distribution by the principle of maximum entropy (POME)

Abstract The principle of maximum entropy (POME) is employed to derive a new method of parameter estimation for the 3-parameter generalized Pareto (GP) distribution. Monte Carlo simulated data are used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). The parameter estimates yielded by the POME ...