Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors

We study a nonlinear measurement model where the response variable has a density belonging to the exponential family. We consider two consistent estimators: Corrected Score (CS) and Quasi Score (QS) ones. Their relative efficiency is compared with respect to asymptotic covariance matrices. We derive...

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Дата:2007
Автор: Malenko, A.
Формат: Стаття
Мова:English
Опубліковано: Інститут математики НАН України 2007
Онлайн доступ:http://dspace.nbuv.gov.ua/handle/123456789/4483
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Назва журналу:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Цитувати:Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors / A. Malenko // Theory of Stochastic Processes. — 2007. — Т. 13 (29), № 1-2. — С. 122-131. — Бібліогр.: 4 назв.— англ.

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Digital Library of Periodicals of National Academy of Sciences of Ukraine
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spelling irk-123456789-44832009-11-20T12:00:30Z Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors Malenko, A. We study a nonlinear measurement model where the response variable has a density belonging to the exponential family. We consider two consistent estimators: Corrected Score (CS) and Quasi Score (QS) ones. Their relative efficiency is compared with respect to asymptotic covariance matrices. We derive expansions of these matrices for small error variances. It is shown that the QS estimator is more efficient than the CS one. The polynomial and Poisson regression models are studied in more detail. 2007 Article Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors / A. Malenko // Theory of Stochastic Processes. — 2007. — Т. 13 (29), № 1-2. — С. 122-131. — Бібліогр.: 4 назв.— англ. 0321-3900 http://dspace.nbuv.gov.ua/handle/123456789/4483 en Інститут математики НАН України
institution Digital Library of Periodicals of National Academy of Sciences of Ukraine
collection DSpace DC
language English
description We study a nonlinear measurement model where the response variable has a density belonging to the exponential family. We consider two consistent estimators: Corrected Score (CS) and Quasi Score (QS) ones. Their relative efficiency is compared with respect to asymptotic covariance matrices. We derive expansions of these matrices for small error variances. It is shown that the QS estimator is more efficient than the CS one. The polynomial and Poisson regression models are studied in more detail.
format Article
author Malenko, A.
spellingShingle Malenko, A.
Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors
author_facet Malenko, A.
author_sort Malenko, A.
title Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors
title_short Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors
title_full Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors
title_fullStr Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors
title_full_unstemmed Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors
title_sort efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors
publisher Інститут математики НАН України
publishDate 2007
url http://dspace.nbuv.gov.ua/handle/123456789/4483
citation_txt Efficiency comparison of two consistent estimators in nonlinear regression model with small measurement errors / A. Malenko // Theory of Stochastic Processes. — 2007. — Т. 13 (29), № 1-2. — С. 122-131. — Бібліогр.: 4 назв.— англ.
work_keys_str_mv AT malenkoa efficiencycomparisonoftwoconsistentestimatorsinnonlinearregressionmodelwithsmallmeasurementerrors
first_indexed 2023-03-24T08:30:21Z
last_indexed 2023-03-24T08:30:21Z
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