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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