Higher Dimensional Nonlinear Regression-A Statistical Use of the Riemannian Curvature Tensor

DOI10.1080/02331889308802428zbMath0811.62062OpenAlexW2077090136WikidataQ115301419 ScholiaQ115301419MaRDI QIDQ4763442

Andrej Pázman

Publication date: 10 April 1995

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331889308802428

zbMATH Keywords

maximum likelihood estimatorarbitrary dimensionRiemannian curvature tensornormal errorsdistribution of estimators

Mathematics Subject Classification ID

General nonlinear regression (62J02) Global Riemannian geometry, including pinching (53C20)

Related Items (3)

Nonlinear regression and curved exponential families. Improvement of the approximation to the asymptotic distribution ⋮ Distribution of the multivariate nonlinear LS estimator under an uncertain input ⋮ Designs in nonlinear regression by stochastic minimization of functionals of the mean square error matrix

Cites Work

• On the density of minimum contrast estimators
• Almost exact distributions of estimators I-low dimensional nonlinear regression
• Almost exact distributions of estimators ii- hat nonlinear regression models
• Invited discussion paper small-sample distributional properties of nonlinear regression estimators (a geometric approach)
• On a formula for the distribution of the maximum likelihood estimator
• The Asymptotic Distribution of Nonlinear Regression Parameter Estimates: Improving the Approximation
• Pivotal variables and confidence regions in flat nonlinear regression models with unknown σ

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