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

Results presented in previous authors papers are extended from the case of a low dimension of the parameter to the case of an arbitrary dimension. In particular, for arbitrary nonlinear regression models with normal errors, we present in an explicit form the "almost exact" density of the m...

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Veröffentlicht in:	Statistics (Berlin, DDR) DDR), 1993-01, Vol.25 (1), p.17-25
1. Verfasser:	Pázman, Andrej
Format:	Artikel
Sprache:	eng
Schlagworte:	AMS 1980 subject classification curvature tensor distribution of estimators maximum likelihood Nonlinear regression
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description	Results presented in previous authors papers are extended from the case of a low dimension of the parameter to the case of an arbitrary dimension. In particular, for arbitrary nonlinear regression models with normal errors, we present in an explicit form the "almost exact" density of the maximum likelihood estimator. It is a better approximation than the one obtained by the saddle-point method. In all obtained results the Riemannian curvature tensor is of great importance.
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subjects	AMS 1980 subject classification curvature tensor distribution of estimators maximum likelihood Nonlinear regression
title	Higher Dimensional Nonlinear Regression-A Statistical Use of the Riemannian Curvature Tensor
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