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Anglický jazyk
Asymmetric distributions and Information Theory
Autor: Javier E. Contreras-Reyes
Data from many applied fields exhibit both heavy tail and skewness behavior. For this reason, in the last few decades, there has been a growing interest in exploring parametric classes of asymmetric distributions. A popular approach to model departure from... Viac o knihe
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O knihe
Data from many applied fields exhibit both heavy tail and skewness behavior. For this reason, in the last few decades, there has been a growing interest in exploring parametric classes of asymmetric distributions. A popular approach to model departure from normality consists of modifying a symmetric probability density function introducing skewness. This allows to measuring the disparity of a particular probability density function from a normal one using information measures. In this monograph, these tools are studied to the full symmetric class of multivariate elliptical and skew-elliptical distributions, and related families. Specifically, the Shannon entropy and negentropy, Kullback-Leibler and Jeffrey's divergences, and Jensen-Shannon distance are developed for these distributions. Finally, the results are applied on several real data sets: a seismological catalogue related to the 2010 Maule earthquake, a optimal design of an ozone monitoring station network, and on biological catalogues of anchovy and swordfish from the coast of Chile.
- Vydavateľstvo: LAP LAMBERT Academic Publishing
- Rok vydania: 2018
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Anglický jazyk
- ISBN: 9786202055772
