Journal Title
Title of Journal: Methodol Comput Appl Probab
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Abbravation: Methodology and Computing in Applied Probability
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Authors: Francisco Javier Alonso María del Carmen Bueso José Miguel Angulo
Publish Date: 2011/10/12
Volume: 14, Issue: 3, Pages: 705-716
Abstract
Risk indicators used in many applications usually involve certain transformations of the variables of interest such as averages or maxima over given time periods or spatial regions threshold exceedances etc or a combination of them A common practice is to predict these indicators by applying the same type of transformation on the sample data that is the ‘historical’ values of the same indicators are used as the sample information set In this work the loss of information derived from the transformations defining the sample set is studied for different indicators and considering a flexible covariance model separating fractal dimension and memory The evaluations and comparisons are performed in terms of predictive mutual information based on Shannon’s entropy The results obtained for different scenarios suggest that depending on the type of risk indicator considered and the dependence structure of the process of interest the changes in terms of predictive information using diverse transformations of the observations may be substantial
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