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 |
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Формат: | Стаття |
Мова: | English |
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Інститут математики НАН України
2007
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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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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 |
_version_ |
1796139185342513152 |