Geometry of exponential type regression models and asymptotic inference

DOI10.1007/BF02662002zbMath0809.62055OpenAlexW2026195312MaRDI QIDQ1319144

Yangming Ma, Bo-Cheng Wei

Publication date: 29 March 1995

Published in: Applied Mathematics. Series B (English Edition) (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02662002

zbMATH Keywords

informationcurvaturestochastic expansionsexponential type regression models

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) General nonlinear regression (62J02) Analysis of variance and covariance (ANOVA) (62J10)

Cites Work

• The geometry of exponential families
• Defining the curvature of a statistical problem (with applications to second order efficiency)
• The geometry of asymptotic inference. With comments and a rejoinder by the author
• Differential geometry of curved exponential families. Curvatures and information loss
• Maximum likelihood estimation and large-sample inference for generalized linear and nonlinear regression models
• Bias in nonlinear regression
• Central Limit Theorems for Families of Sequences of Random Variables
• Geometrical theory of asymptotic ancillarity and conditional inference
• SOME SECOND ORDER ASYMPTOTICS IN NONLINEAR REGRESSION
• Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
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