# Geometrical Foundations of Asymptotic Inference. Wiley Series in Probability and Statistics

• ID: 2181438
• Book
• 376 Pages
• John Wiley and Sons Ltd
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Differential geometry provides an aesthetically appealing and often revealing view of statistical inference. Beginning with an elementary treatment of one–parameter statistical models and ending with an overview of recent developments, this is the first book to provide an introduction to the subject that is largely accessible to readers not already familiar with differential geometry. It also gives a streamlined entry into the field to readers with richer mathematical backgrounds. Much space is devoted to curved exponential families, which are of interest not only because they may be studied geometrically but also because they are analytically convenient, so that results may be derived rigorously. In addition, several appendices provide useful mathematical material on basic concepts in differential geometry. Topics covered include the following:
• Basic properties of curved exponential families
• Elements of second–order, asymptotic theory
• The Fisher–Efron–Amari theory of information loss and recovery
• Jeffreys–Rao information–metric Riemannian geometry
• Curvature measures of nonlinearity
• Geometrically motivated diagnostics for exponential family regression
• Geometrical theory of divergence functions
• A classification of and introduction to additional work in the field
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Overview and Preliminaries.

ONE–PARAMETER CURVED EXPONENTIAL FAMILIES.

First–Order Asymptotics.

Second–Order Asymptotics.

MULTIPARAMETER CURVED EXPONENTIAL FAMILIES.

Extensions of Results from the One–Parameter Case.

Exponential Family Regression and Diagnostics.

Curvature in Exponential Family Regression.

DIFFERENTIAL–GEOMETRIC METHODS.

Information–Metric Riemannian Geometry.

Statistical Manifolds.

Divergence Functions.

Recent Developments.

Appendices.

References.

Indexes.
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