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Note on the strong consistency of the least squares estimator in nonlinear regression

 

作者: Henning Läuter,  

 

期刊: Statistics  （Taylor Available online 1989）
卷期: Volume 20, issue 2  

页码: 199-210

 

ISSN:0233-1888

 

年代: 1989

 

DOI:10.1080/02331888908802161

 

出版商: Akademie-Verlag

 

关键词: 62J05;60F15;Strong law of large numbers;consistency;nonlinear regression;limit distribution

 

数据来源: Taylor

 

摘要:

We consider a nonlinear regression model under standard assumptions on the error distribution, We prove an almost sure convergence of weighted sums with an interesting uniformity, and under very general conditions on the parameter space and the regression function we prove the a.s, boundedness and the strong consistency of the least squares estimator, Here we generalize results of Jennrich (1969) to unbounded parameter spaces

 

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