Full Information Optimal Scoring
نویسندگان
چکیده
منابع مشابه
Optimal Scoring for Unsupervised Learning
We are often interested in casting classification and clustering problems as a regression framework, because it is feasible to achieve some statistical properties in this framework by imposing some penalty criteria. In this paper we illustrate optimal scoring, which was originally proposed for performing the Fisher linear discriminant analysis by regression, in the application of unsupervised l...
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When additional information sources are available in decision making problems that allow stochastic optimization formulations, an important question is how to optimally use the information the sources are capable of providing. A framework that relates information accuracy determined by the source's knowledge structure to its relevance determined by the problem being solved was proposed in a com...
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In this paper we combine the theory of probability aggregation with results of machine learning theory concerning the optimality of predictions under expert advice. In probability aggregation theory several characterisation results for linear aggregation exist. However, in linear aggregation weights are not fixed, but free parameters. We show how fixing such weights by successbased scores allow...
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In a world with private information and learning on the part of the market participants, the (positive) difference between the observed transaction price of an asset and the corresponding unobserved full-information price (the price that reflects private and public information about the asset) represents an ideal measure of market quality. We call this difference “full-information transaction c...
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ژورنال
عنوان ژورنال: Journal of Educational and Behavioral Statistics
سال: 2019
ISSN: 1076-9986,1935-1054
DOI: 10.3102/1076998619885636