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Stronger estimations of Csiszar f-divergences

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Title: Stronger estimations of Csiszar f-divergences
Authors: Ivelić Bradanović, Slavica
Publisher Information: 2023.
Publication Year: 2023
Subject Terms: Csizar f-divergences, Kullback-Leibler divergence, Hellinger divergence, Strongly convex functions
Description: In many problems in statistics, closeness/similarity between two probability distributions needs to be measured. To solve such problems, various statistical divergences are introduced as essential and general tool for comparison of two distributions. A statistical divergence D(p,q), as mapping of two probability distributions p and q to R, satisfies conditions D(p,q)≥0 and D(p,q)=iff p=q. Two distributions p and q are very similar if D(p,q) is very close to zero. One important class of statistical divergence is defined by means of convex functions and is known as Csiszár f-divergence. In our work, we establish stronger estimations of Csiszar f-divergences between two distributions by using the class of strongly convex functions, a subclass of convex functions with stronger versions of analogous properties. As outcome we derive stronger estimates for some well known divergences as the Kullback-Leibler divergence, χ-divergence, Hellinger divergence, Bhattacharya distance and Jeffreys distance.
Document Type: Conference object
Accession Number: edsair.dris...01492..24f9a70cf5b80883202e22b6bc19431c
Database: OpenAIRE
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  Data: Stronger estimations of Csiszar f-divergences
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Ivelić+Bradanović%2C+Slavica%22">Ivelić Bradanović, Slavica</searchLink>
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  Data: 2023.
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  Label: Publication Year
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  Data: 2023
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  Data: <searchLink fieldCode="DE" term="%22Csizar+f-divergences%22">Csizar f-divergences</searchLink><br /><searchLink fieldCode="DE" term="%22Kullback-Leibler+divergence%22">Kullback-Leibler divergence</searchLink><br /><searchLink fieldCode="DE" term="%22Hellinger+divergence%22">Hellinger divergence</searchLink><br /><searchLink fieldCode="DE" term="%22Strongly+convex+functions%22">Strongly convex functions</searchLink>
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  Label: Description
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  Data: In many problems in statistics, closeness/similarity between two probability distributions needs to be measured. To solve such problems, various statistical divergences are introduced as essential and general tool for comparison of two distributions. A statistical divergence D(p,q), as mapping of two probability distributions p and q to R, satisfies conditions D(p,q)≥0 and D(p,q)=iff p=q. Two distributions p and q are very similar if D(p,q) is very close to zero. One important class of statistical divergence is defined by means of convex functions and is known as Csiszár f-divergence. In our work, we establish stronger estimations of Csiszar f-divergences between two distributions by using the class of strongly convex functions, a subclass of convex functions with stronger versions of analogous properties. As outcome we derive stronger estimates for some well known divergences as the Kullback-Leibler divergence, χ-divergence, Hellinger divergence, Bhattacharya distance and Jeffreys distance.
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  Data: edsair.dris...01492..24f9a70cf5b80883202e22b6bc19431c
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      – Text: Undetermined
    Subjects:
      – SubjectFull: Csizar f-divergences
        Type: general
      – SubjectFull: Kullback-Leibler divergence
        Type: general
      – SubjectFull: Hellinger divergence
        Type: general
      – SubjectFull: Strongly convex functions
        Type: general
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      – TitleFull: Stronger estimations of Csiszar f-divergences
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            NameFull: Ivelić Bradanović, Slavica
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          Dates:
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              M: 01
              Type: published
              Y: 2023
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