Results 1  10
of
10
K.: Evaluating Demand Prediction Techniques for Computational Markets
 In: GECON ’06: Proceedings of the 3rd International Workshop on Grid Economics and Business Models
, 2006
"... We evaluate different prediction techniques to estimate future demand of resource usage in a computational market. Usage traces from the PlanetLab network are used to compare the prediction accuracy of models based on histograms, normal distribution approximation, maximum entropy, and autoregression ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We evaluate different prediction techniques to estimate future demand of resource usage in a computational market. Usage traces from the PlanetLab network are used to compare the prediction accuracy of models based on histograms, normal distribution approximation, maximum entropy, and autoregression theory. We particularly study the ability to predict the tail of the probability distribution in order to give guarantees of upper bounds of demand. We found that the maximum entropy model was particularly well suited to predict these upper bounds. 1.
Computing Maximum Entropy Densities: A Hybrid Approach
"... hujinchun @ tsinghua.edu.cn ..."
(Show Context)
GMM Estimation of a Maximum Entropy
, 2005
"... eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide. ..."
Abstract
 Add to MetaCart
(Show Context)
eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide.
GMM Estimation of a Maximum Entropy Distribution with Interval Data
, 2005
"... We develop a GMM estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first ..."
Abstract
 Add to MetaCart
(Show Context)
We develop a GMM estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a firststep consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. Our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The twostep GMM estimator with a simulated weighting matrix improves the efficiency of the onestep GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlying raw income data.
Abstract Maximum Entropy Density Estimation Using a Genetic Algorithm
"... Several unsupervised learning algorithms, neural networks, and support vector machine based classification and clustering approaches are kernelbased, and require sophisticated algorithms for density estimation. The density estimation problem is a nontrivial optimization problem and most of the exis ..."
Abstract
 Add to MetaCart
Several unsupervised learning algorithms, neural networks, and support vector machine based classification and clustering approaches are kernelbased, and require sophisticated algorithms for density estimation. The density estimation problem is a nontrivial optimization problem and most of the existing density estimation algorithms provide locally optimal solutions. In this paper we use an entropy maximizing approach that uses global search genetic algorithm to estimate densities for a given data set. Unlike the traditional local search approaches, our approach uses global search and is more likely to provide solutions that are close to global optimum. Using a simulated dataset, we compare the results of our approach with the maximum likelihood approach. 1.
InformationTheoretic Distribution Test with Application to Normality
"... We derive general distribution tests based on the method of Maximum Entropy density. The proposed tests are derived from maximizing the differential entropy subject to moment constraints. By exploiting the equivalence between the Maximum Entropy and Maximum Likelihood estimates of the general expone ..."
Abstract
 Add to MetaCart
We derive general distribution tests based on the method of Maximum Entropy density. The proposed tests are derived from maximizing the differential entropy subject to moment constraints. By exploiting the equivalence between the Maximum Entropy and Maximum Likelihood estimates of the general exponential family, we can use the conventional Likelihood Ratio, Wald and Lagrange Multiplier testing principles in the maximum entropy framework. In particular, we use the Lagrange Multiplier method to derive tests for normality and their asymptotic properties. Monte Carlo evidence suggests that the proposed tests have desirable small sample properties and often outperform commonly used tests such as the JarqueBera test and the KolmogorovSmirnovLillie test for normality. We show that the proposed tests can be extended to tests based on regression residuals and noniid data in a straightforward manner. We apply the proposed tests to the residuals from a stochastic production frontier model and reject the normality hypothesis.
INFORMATIONTHEORETIC DISTRIBUTION TEST WITH APPLICATION TO NORMALITY
, 2010
"... This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sublicensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express o ..."
Abstract
 Add to MetaCart
This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sublicensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
unknown title
"... estimate a distribution when only limited information about intervals or ranges is available. ARTICLE IN PRESS www.elsevier.com/locate/jeconom ..."
Abstract
 Add to MetaCart
(Show Context)
estimate a distribution when only limited information about intervals or ranges is available. ARTICLE IN PRESS www.elsevier.com/locate/jeconom
Optimal predictive densities and fractional moments
, 2016
"... Optimal predictive densities and fractional moments ..."
(Show Context)
Article Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach
, 2014
"... www.mdpi.com/journal/econometrics ..."
(Show Context)