Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Wolfgang Stummer"'
Autor:
Michel Broniatowski, Wolfgang Stummer
Publikováno v:
Entropy, Vol 26, Iss 4, p 312 (2024)
It is well known that in information theory—as well as in the adjacent fields of statistics, machine learning and artificial intelligence—it is essential to quantify the dissimilarity between objects of uncertain/imprecise/inexact/vague informati
Externí odkaz:
https://doaj.org/article/e708bb78908c41d7a0a19c90d2467276
Autor:
Niels B. Kammerer, Wolfgang Stummer
Publikováno v:
Entropy, Vol 22, Iss 8, p 874 (2020)
We compute exact values respectively bounds of dissimilarity/distinguishability measures–in the sense of the Kullback-Leibler information distance (relative entropy) and some transforms of more general power divergences and Renyi divergences–betw
Externí odkaz:
https://doaj.org/article/6f9cda4882464b60abd7f1203331428f
Autor:
Wolfgang Stummer
Publikováno v:
Entropy, Vol 3, Iss 5, Pp 300-324 (2001)
Abstract: We consider asset price processes Xt which are weak solutions of one-dimensional stochastic differential equations of the form (equation (2)) Such price models can be interpreted as non-lognormally-distributed generalizations of the geometr
Externí odkaz:
https://doaj.org/article/0395207d8c9244f985291246a01fd2d9
Autor:
Michel Broniatowski, Wolfgang Stummer
Publikováno v:
Handbook of Statistics ISBN: 9780323913454
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73d5b524990ff7e52dbfad096f252603
https://doi.org/10.1016/bs.host.2022.03.007
https://doi.org/10.1016/bs.host.2022.03.007
Autor:
Wolfgang Stummer
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030802080
GSI
GSI
We present a toolkit of directed distances between quantile functions. By employing this, we solve some new optimal transport (OT) problems which e.g. considerably flexibilize some prominent OTs expressed through Wasserstein distances.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5162670de97d0cac84dcb4ebf0825b9a
https://doi.org/10.1007/978-3-030-80209-7_89
https://doi.org/10.1007/978-3-030-80209-7_89
Autor:
Michel Broniatowski, Wolfgang Stummer
In information theory -- as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition -- many flexibilizations of the omnipresent Kullback-Leibler information distance (relative
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::711201a3f8166d47e886018710eb9e1b
Autor:
Wolfgang Stummer, Anna-Lena Kißlinger
Publikováno v:
Applied Stochastic Models in Business and Industry. 34:682-699
Autor:
Wolfgang Stummer, Sarah Krömer
Publikováno v:
Springer Proceedings in Mathematics & Statistics ISBN: 9783030286644
For the calculation of premiums, financial reserves, annuities, pension benefits, various benefits of social insurance programs, and many other quantities, a realistic representation of mortality rates is of fundamental essence. We achieve this by a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07124637ffc9d098b133b5eb2741ceeb
https://doi.org/10.1007/978-3-030-28665-1_30
https://doi.org/10.1007/978-3-030-28665-1_30
Autor:
Wolfgang Stummer, Birgit Roensch
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030269791
GSI
GSI
In the separate Part I (see [23]), we have derived a new robustness-featured parameter-estimation framework, in terms of minimization of the scaled Bregman power distances of Stummer and Vajda [25] (see also [24]); this leads to a wide range of outli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6466e3e8013821b0e40789760a25fcfa
https://doi.org/10.1007/978-3-030-26980-7_34
https://doi.org/10.1007/978-3-030-26980-7_34
Autor:
Wolfgang Stummer, Birgit Roensch
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030269791
GSI
GSI
In contemporary data analytics, one often models uncertainty-prone data as samples stemming from a sequence of independent random variables whose distributions are non-identical but linked by a common (scalar or multidimensional) parameter. For such
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1d25d399ba6ecf7eac02e67b812481f
https://doi.org/10.1007/978-3-030-26980-7_33
https://doi.org/10.1007/978-3-030-26980-7_33