Kass R.E., Vos P.W. Geometrical Foundations of Asymptotic Inference

New York: Wiley-Interscience, 1997. — 378 p.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