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Stochastic approximation type estimators in linear models

 

作者: M. Hušková,  

 

期刊: Sequential Analysis  (Taylor Available online 1991)
卷期: Volume 10, issue 1-2  

页码: 45-68

 

ISSN:0747-4946

 

年代: 1991

 

DOI:10.1080/07474949108836225

 

出版商: Marcel Dekker, Inc.

 

数据来源: Taylor

 

摘要:

A new class of stochastic approximation type estimators, different from those obtained via Robbins-Monro procedure, is introduced. Their asymptotic properties are studied. The proposed estimator is on the n-th step defined as the one step version M-estimator, where the estimator from the previous step is used as a preliminary one. This type of estimators is particularly useful in testing of constancy of the regression relationship over time.

 

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