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- The role of differential geometry in statistical theory
- Information geometry
- Differential Geometry And Statistics pdf
- The Differential Geometric View of Statistics and Estimation
Marriott, Paul Applications of differential geometry to statistics.
The role of differential geometry in statistical theory
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Barndorff-Nielsen and D. Cox and N. Barndorff-Nielsen , D. Cox , N. On donne quelques idees de la geometrie differentielle et quelques applications. Puis on donne un apercu des travaux recents appliquant la geometrie differentielle a la theorie asymptotique de l'inference statistique. View PDF. Save to Library. Create Alert. Launch Research Feed.
Share This Paper. Methods Citations. Citation Type. Has PDF. Publication Type. More Filters. A geometric approach to singularity for Hilbert space-valued SDEs. Research Feed. Statistical Manifolds of univariate elliptic distributions. Efficiency and robustness in a geometrical perspective.
On the role of differential geometry in signal processing. View 1 excerpt, cites background. Asymptotics and the theory of inference.
An elementary account of Amari's expected geometry. An Elementary Introduction to Information Geometry. On preferred point geometry in statistics. A Riemannian geometry of the multivariate normal model. View 2 excerpts, references background. View 1 excerpt, references background. Invariants and likelihood ratio statistics. View 1 excerpt, references methods. Indirectional statistics and the significance of an asymmetry discovered by Birch.
Geometrical theory of asymptotic ancillarity and conditional inference. Highly Influential. View 8 excerpts, references background and methods.
Information geometry is an interdisciplinary field that applies the techniques of differential geometry to study probability theory and statistics. It studies statistical manifolds , which are Riemannian manifolds whose points correspond to probability distributions. Historically, information geometry can be traced back to the work of C. Rao , who was the first to treat the Fisher matrix as a Riemannian metric. Classically, information geometry considered a parametrized statistical model as a Riemannian manifold.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. From differential to information geometry Abstract: Information geometry is a new and increasing topic between statistics, estimation and differential geometry. Many amazing relationships between these domains were established through the last years.
Differential Geometry And Statistics pdf
It seems that you're in Germany. We have a dedicated site for Germany. From the reviews: "In this Lecture Note volume the author describes his differential-geometric approach to parametrical statistical problems summarizing the results he had published in a series of papers in the last five years. The author provides a geometric framework for a special class of test and estimation procedures for curved exponential families. The material and ideas presented in this volume are important and it is recommended to everybody interested in the connection between statistics and geometry
Rice The first chapter of the book explores exponential families as flat geometries. Indeed the whole notion of using log-likelihoods amounts to exploiting a particular form of flat space known as an affine geometry, in which straight lines and planes make sense, but lengths and angles are absent. We use these geometric ideas to introduce the notion of the second fundamental form of a family whose vanishing characterises precisely the exponential families.
This a slightly misleading name for applying differential geometry to families of probability distributions , and so to statistical models. Information does however play two roles in it: Kullback-Leibler information, or relative entropy , features as a measure of divergence not quite a metric, because it's asymmetric , and Fisher information takes the role of curvature.
The Differential Geometric View of Statistics and Estimation
Theme By Yeei! Differential Geometry and Statistics. Murray , J.
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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Statistics and estimation theory is enriched with techniques derived from differential geometry. This establishes the increasing topic of information geometry. This allows new insights into these classical topics. Differential geometry offers a wide spectrum of applications within statistic inference and estimation theory.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Barndorff-Nielsen and D.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Barndorff-Nielsen and D. Cox and N.
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