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Geometry in statistics
Author(s) -
Vos Paul W.,
Marriott Paul
Publication year - 2010
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.128
Subject(s) - information geometry , statistical inference , nonparametric statistics , euclidean geometry , geometry , computer science , focus (optics) , inference , mathematics , statistics , artificial intelligence , physics , scalar curvature , curvature , optics
Abstract Geometry is a broad area that has applications to many areas of statistics. In this article the focus will be on the role of dual information geometries to statistical inference. A great deal of research has been done in the application of these dual geometries to higher order asymptotics and a brief review is given. Greater attention is given to providing insight into dual geometries as extensions of Euclidean geometry, and how, a further extension, called the dual simplicial geometry, can provide a general framework for computational algorithms. WIREs Comp Stat 2010 2 686–694 DOI: 10.1002/wics.128 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Information Theoretic Methods Statistical and Graphical Methods of Data Analysis > Nonparametric Methods