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

 

作者: Andrej Pázman,  

 

期刊: Statistics  （Taylor Available online 1993）
卷期: Volume 25, issue 1  

页码: 17-25

 

ISSN:0233-1888

 

年代: 1993

 

DOI:10.1080/02331889308802428

 

出版商: Gordon & Breach Science Publishers

 

关键词: AMS 1980 subject classification;62J02;62F11;53C20;Nonlinear regression;maximum likelihood;distribution of estimators;curvature tensor

 

数据来源: Taylor

 

摘要:

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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