@techreport{Papalia2003Modeling,
abstract = {In this study we illustrate a Maximum Entropy (ME) methodology for modeling
incomplete information and learning from repeated samples. The basis for this
method has its roots in information theory and builds on the classical maximum
entropy work of Janes (1957). We illustrate the use of this approach, describe how
to impose restrictions on the estimator, and how to examine the sensitivity of ME
estimates to the parameter and error bounds. Our objective is to show how
empirical measures of the value of information for microeconomic models can be
estimated in the maximum entropy view.},
author = {Rosa Bernardini Papalia},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {330; Generalized Maximum Entropy; Generalized Cross Entropy; Repeated Samples; Microeconometric models; Stichprobenverfahren; Entropie; Mikro\"{o}konometrie; Theorie},
language = {eng},
note = {urn:nbn:de:kobv:11-10050297},
number = {2003,29},
title = {Modeling the Learning from Repeated Samples: A Generalized Cross Entropy Approach},
type = {Discussion papers of interdisciplinary research project 373},
url = {http://hdl.handle.net/10419/22244},
year = {2003}
}
